Bridging Academic and Career Development: A Longitudinal Examination of Engagement,Achievement,and Career Adaptability

Abstract

Grounded in the adaptive career construction model, this study investigated the developmental trajectories of academic engagement, academic achievement, and career adaptability, as well as the longitudinal effects of academic engagement and achievement on career adaptability. A total of 1,243 Chinese university students (52% female; Mage = 19.53, SD = 0.88) were recruited through convenience sampling. Over a two-year period, data were collected at four six-month intervals. Results revealed that students’ career adaptability declined over time, whereas both major and non-major academic engagement increased. Major academic achievement remained stable, while non-major academic achievement declined. Growth curve analyses further showed that the initial levels and developmental trajectories of both major and non-major academic engagement and achievement positively predicted the initial level and changes in career adaptability. Notably, the initial level of major academic achievement exerted a sustained positive influence on changes in career adaptability, highlighting its stronger role in shaping adaptability. These findings extend career construction theory and provide practical insights for integrating academic education with career development.

Keywords

academic engagement academic achievement career adaptability developmental trajectory university students

Introduction

Since the beginning of the 21st century, China’s higher education system has expanded rapidly. The number of university graduates has increased from 1.15 million in 2001 to 11.79 million in 2024, intensifying competition in the job market. As a result, the phenomenon of “employment first, career selection later” has become increasingly common. Approximately one-third of graduates are employed in fields unrelated to their academic majors (Wei, 2017), which undermines employment quality, job satisfaction, and career stability (DeLoach & Kurt, 2018; Somers et al., 2019). At the same time, rapid technological developments—such as automation and artificial intelligence—are transforming occupational structures, leading to the decline of traditional jobs and the creation of new career paths. These shifts require workers to continually adapt to evolving demands, further heightening career uncertainty (Jia et al., 2024). In this context, university students’ career adaptability becomes crucial. High levels of career adaptability can facilitate smoother school-to-work transitions, support informed career decision-making, enhance employment quality, and prepare individuals for ongoing workplace transformations (Koen et al., 2012). Therefore, understanding the development of career adaptability and its influencing factors among university students is of paramount importance.

Career adaptability refers to an individual’s readiness and resources for managing current and anticipated vocational tasks, transitions, and challenges (Savickas, 2005). It is considered a core component of career development. The career construction model conceptualizes career adaptability as part of a broader developmental sequence—adaptive readiness, adaptability resources, adapting responses, and adaptation results—where adaptability resources (i.e., career adaptability) are shaped primarily by adaptive readiness (Savickas, 2012, 2013). While this model has received substantial empirical support, prior research has largely emphasized psychological traits such as proactivity and self-evaluations (Hirschi et al., 2015), hope and optimism (Wilkins et al., 2014), and sense of purpose (Praskova et al., 2014). More recently, scholars have argued for the inclusion of educational and training experiences as critical forms of adaptive readiness, calling for an expanded conceptualization that incorporates academic factors (Johnston, 2018; Rudolph et al., 2017).

To date, only a limited number of studies have examined the predictive role of academic engagement and achievement in shaping career adaptability at different educational levels (Datu & Buenconsejo, 2021; Hsieh, 2025; Negru-Subtirica & Pop, 2016; Šverko & Babarovic´, 2019; Shaheen et al., 2024). These studies, informed by the adaptive career construction model, broaden our understanding of the determinants of career adaptability by integrating academic factors into the framework of adaptive readiness. Nonetheless, several important gaps remain. First, university learning differs substantially from high school learning, involving both major-related and non-major-related dimensions. Major-related learning refers to courses directly tied to the professional curriculum (e.g., foundational, core, and advanced courses). These courses form a progressive, compulsory system with credit-based recognition. In contrast, non-major-related learning refers to supplementary general education courses across fields such as humanities, social sciences, and natural sciences. These courses are typically voluntary, interdisciplinary, and non-compulsory (Ministry of Education of the People’s Republic of China, 2022). While major-related learning emphasizes deep specialization and “hard skills” needed for employment, non-major-related learning broadens knowledge, fosters transferable skills, and supports adaptability in complex work environments (Bok, 2006). Despite their distinct developmental functions, empirical research systematically comparing the effects of these two learning dimensions on career adaptability is lacking. Second, although existing research has independently examined academic engagement and academic achievement, these constructs capture different aspects of learning. Academic engagement reflects the learning process, whereas academic achievement represents learning outcomes. Given their distinct developmental patterns, they may play different roles in predicting career adaptability. However, limited research has investigated the differential effects of these two constructs on the development of career adaptability among university students.

To address these gaps, the present study investigates the developmental trajectories of academic engagement, academic achievement, and career adaptability among Chinese university students, with particular attention to both major- and non-major-related learning. It further examines how changes in academic engagement and achievement influence the development of career adaptability over time. By providing longitudinal evidence within the academic domain, this study extends the adaptive career construction model and offers practical insights for integrating academic education with career development interventions.

Developmental Trajectory of Career Adaptability

University students are in a critical transitional stage from academia to the workforce, and the development of their career adaptability plays an essential role in helping them effectively navigate career challenges and adjust to workplace environments (Pastore & Zimmermann, 2019). According to career construction theory (Savickas, 1997, 2005, 2013), career adaptability is conceptualized as a dynamic and malleable construct that evolves as individuals grow and develop.

Most longitudinal studies suggest that career adaptability tends to increase over time among university students. For instance, a six-month study reported a steady improvement in career adaptability levels (Autin et al., 2017). Similarly, Jiang (2013) found that sophomore and senior students exhibited significantly higher adaptability at Time 2 than at Time 1 over a 6-month period. Research has also shown that career adaptability follows a linear upward trajectory during the first year of transition from university to the workforce (Fu et al., 2023). However, these studies are relatively short in duration and may not fully capture the developmental patterns of career adaptability across the entire university period.

Other evidence suggests that career adaptability may not follow a purely linear trajectory. For example, cross-sectional findings indicate a V-shaped pattern, with higher scores among first-year and final-year students and lower scores among sophomores and juniors (Yan, 2018). This suggests that academic and career-related experiences at different stages of university life may shape the development of career adaptability in diverse ways. To address these gaps, the present study conducted a two-year longitudinal investigation following first-year and second-year students. Based on prior research, we hypothesize the following.

Hypothesis 1

Over the two-year study period, career adaptability is expected to show a continuous decline among first-year students but a continuous increase among second-year students.

Developmental Trends of Academic Engagement and Academic Achievement

Academic engagement and academic achievement are critical indicators of university students’ learning experiences and are closely related to their future careers. Consequently, they have attracted substantial attention from families, educational institutions, and researchers (Alrashidi et al., 2016; Negru-Subtirica & Pop, 2016).

Academic engagement is conceptualized as a positive and fulfilling psychological state characterized by vigor, dedication, and absorption (Schaufeli et al., 2002). As a process variable, it reflects students’ involvement in learning and is highly sensitive to influences from both individual and contextual factors (Zusho, 2017). Findings on changes in academic engagement across university years are mixed. Some cross-sectional studies have reported that freshmen exhibit significantly higher engagement than juniors and seniors, with sophomores showing only a slight, non-significant decline (Cui, 2013). Other studies have found that seniors report the highest engagement, suggesting a potential increase as students progress (Han, 2014). Longitudinal research has also indicated an overall upward trend across 4 years of university (Yang & Han, 2013). However, these studies have typically examined general engagement without differentiating between major-related and non-major-related coursework. Evidence focusing specifically on major-related engagement suggests that levels remain relatively stable across academic years (Fu, 2016). In most Chinese universities, general education (i.e., non-major) courses are concentrated in the first two years, with their proportion declining thereafter (Li, 2008). Moreover, as students advance, increasing pressures of employment and postgraduate entrance exams may lead them to allocate more time and effort to major-related coursework (Pascarella & Terenzini, 2005). Accordingly, engagement in non-major coursework is likely to decrease.

Hypothesis 2

Engagement in major-related coursework is expected to remain relatively stable over time, whereas engagement in non-major coursework is expected to decline.

As an outcome variable, academic achievement reflects students’ learning effectiveness (Zusho, 2017). Prior studies have generally found that academic achievement remains stable over time. For example, an 11-year longitudinal study reported that students’ math and reading performance showed little change across elementary and secondary school (McCoach et al., 2017). Similarly, cross-sectional studies of university students have found no significant differences across academic years (Zhu, 2012). Yet, academic achievement in university is more complex than in earlier education, encompassing both major-related and non-major-related coursework. Existing research has rarely examined the trajectories of these two components separately. Given that major academic achievement is directly tied to professional competence and highly valued by employers (Feng, 2013), students are likely to maintain relatively stable performance in major-related coursework. In contrast, performance in non-major coursework may decline as students gradually reduce their attention to such courses.

Hypothesis 3

Major academic achievement is expected to remain stable over time, whereas non-major academic achievement is expected to decline.

The Impact of the Developmental Trends of Academic Engagement and Academic Achievement on Career Adaptability

There is ongoing debate regarding whether academic factors should be considered antecedents of career adaptability. In recent years, many studies have conceptualized academic factors primarily as outcome variables (e.g., Chen & Wang, 2024; Wang et al., 2024). However, in the context of higher education, a central developmental task for university students is to accumulate career capital through academic achievement (Hirschi, 2012). It is therefore critical to examine how academic competencies developed in educational contexts can be transformed into adaptability in career contexts. According to Savickas’ career construction model of adaptation, academic engagement and academic achievement, as indicators of adaptive readiness, may play an essential role in shaping career adaptability, which functions as an adaptive resource (Johnston, 2018; Marciniak et al., 2022; Rudolph et al., 2017).

Academic engagement has been shown to be critical not only for academic success but also for career development (Kahu, 2013; Wang & Eccles, 2013; Zepke, 2014; Zusho, 2017). Higher levels of engagement in learning improve students’ knowledge base and skill set, thereby enhancing their competitiveness in the job market and enabling them to secure higher-quality employment opportunities (Upadyaya & Salmela-Aro, 2013). Furthermore, active participation in learning activities enhances adaptability resources, supporting career preparation and development (Dietrich et al., 2012; Vuolo et al., 2013; Zacher, 2014). Although few studies have directly examined the effects of major- and non-major academic engagement on career adaptability, recent evidence indicates that overall engagement among university students positively predicts their career adaptability (Shaheen et al., 2024). Building on this evidence, we hypothesize that both the dynamic changes in university students’ academic engagement in major-related and non-major coursework may positively predict changes in their career adaptability over time.

Hypothesis 4

The initial levels of major and non-major academic engagement will positively predict the initial level and developmental trajectory of career adaptability. Additionally, the developmental trajectories of academic engagement will positively predict the developmental trajectory of career adaptability.

Academic achievement is also closely linked to career development, as it influences students’ career beliefs and behaviors (Zhang, 2011). Higher academic achievement enhances personal skills, strengthens career interests, and shapes career expectations, thereby facilitating the school-to-work transition (Vuolo et al., 2013). From the perspective of the career construction model, academic achievement represents a form of adaptive readiness that may positively predict career adaptability (Savickas, 2013). Supporting this view, a recent longitudinal study found that first-year students with higher class rankings demonstrated greater career adaptability in their second year (Hsieh, 2025).

Although limited research has explored the distinct contributions of major- and non-major academic achievement to career adaptability, available evidence suggests that both are important. High levels of major-related academic achievement facilitate career paths aligned with students’ academic backgrounds, promoting job stability and well-being. Conversely, higher non-major academic achievement may broaden students’ interests, fostering greater openness toward diverse career opportunities (Zacher, 2014). Taken together, these findings suggest that the dynamic changes in major-related and non-major academic achievement may positively predict changes in career adaptability over time.

Hypothesis 5

The initial levels of major and non-major academic achievement are expected to positively predict the initial level and developmental trajectory of career adaptability. In addition, the developmental trajectories of academic achievement are expected to positively predict the developmental trajectory of career adaptability.

Method

Participants and Procedure

This study adopted a convenience sampling method to recruit 1,539 university students from two provincial-level universities in Shandong Province, China. Participants were drawn from both liberal arts and science majors. Among them, 797 were first-year students (51.79%), and 742 were second-year students (48.21%). The sample consisted of 736 males (47.82%) and 803 females (52.18%), with an average age of 19.53 years (SD = 0.88).

Data were collected through online surveys administered via Wenjuanxing (WJX, https://www.wjx.cn/). Participants completed the questionnaires within a designated time window. A total of four waves of data collection were conducted over a two-year period, from June 2022 to December 2023, with 6-month intervals between waves. During the longitudinal study, 296 participants dropped out due to reasons such as illness, changes of major, military service, academic leave, or grade retention. Specifically, attrition at Waves 2, 3, and 4 was 192, 67, and 37 participants, accounting for 64.86%, 22.64%, and 12.50% of the total attrition, respectively. The highest attrition occurred during the second wave, primarily due to a sudden university closure in December 2022 caused by the COVID-19 pandemic, which prevented some students from completing the survey. Ultimately, 1,243 participants completed all four waves of data collection.

To assess potential attrition bias, independent-samples t-tests were conducted to compare participants who remained in the study with those who dropped out on key baseline variables, including major and non-major academic engagement, major and non-major academic achievement, and career adaptability. Results revealed no significant differences (p > 0.05) across most variables, except for non-major academic achievement, which approached marginal significance (p = 0.06). Therefore, the final analyses were based on the 1,243 participants who completed all four waves.

In addition, end-of-semester academic records were collected at each wave. Based on the undergraduate curriculum structure, course grades were classified into major-related and non-major categories. The average scores for each category were calculated and standardized within each major, yielding Z-scores for further analyses.

Prior to the formal data collection, approval was obtained from the participating universities. The research team provided training for survey administrators to ensure standardization of the data collection procedures. Before each survey session, administrators explained the study purpose and requirements, assured participants of strict confidentiality, and obtained informed consent before participants proceeded to complete the online questionnaire.

Measures

Academic engagement. Students’ academic engagement in both major and non-major coursework was measured using the Utrecht Work Engagement Scale–Student (UWES-S), developed by Schaufeli et al. (2002). Following the instructions, participants rated each item separately according to their actual engagement in major-related and non-major courses. The scale consists of three dimensions: vigor (e.g., “Even when studying is not going well, I do not get discouraged and can persevere”; 6 items), dedication (e.g., “I am enthusiastic about my studies”; 5 items), and absorption (e.g., “When studying, I forget everything else around me”; 6 items), totaling 17 items. All items were scored on a 7-point Likert scale (1 = never, 7 = always), with higher scores reflecting higher engagement. The scale has been widely applied among Chinese university students and has shown strong psychometric properties. In this study, Cronbach’s α coefficients ranged from 0.94 to 0.98 for both major-related and non-major academic engagement across the four time points (T1–T4).

Academic achievement. Participants’ end-of-semester final exam scores were collected from university records at each of the four time points (T1–T4). To ensure comparability, scores were processed according to the “Undergraduate Training Plan” of each participant’s major. Specifically: (1) Courses were screened based on the training plan, excluding online courses and retaining only in-person classes. (2) Courses were classified as major-related (required + elective) or non-major (required + elective). (3) Given that students may take different elective courses under the credit system, individual average scores for both categories were calculated for each participant based on the number and scores of courses taken. (4) Scores were then standardized within each major to yield Z-scores for both major-related and non-major academic achievement.

Career adaptability. The Career Adapt-Abilities Scale (CAAS), developed by Savickas and Porfeli (2012), was used to measure individuals’ career adaptability. The scale includes four dimensions: career concern (e.g., “Thinking about what my future will be like”), career control (e.g., “Taking responsibility for my actions”), career curiosity (e.g., “Exploring my areas of interest in depth”), and career confidence (e.g., “Doing things thoroughly and well”), with 6 items per dimension, totaling 24 items. All items are scored on a 5-point Likert scale (1 = not strong, 5 = very strong), with higher scores indicating higher levels of career adaptability. The scale has been widely used among Chinese university students and demonstrates good reliability and validity. In this study, the Cronbach’s α coefficients for each dimension across T1-T4 time points ranged from 0.93 to 0.98.

Control variables. Consistent with prior research (Fu et al., 2023), demographic variables including age and gender were controlled in all analyses to account for potential confounding effects.

Data Analysis

Descriptive statistics and correlation analyses were conducted using SPSS 26.0. To examine developmental trajectories, Latent Growth Models (LGM) were estimated in Mplus 8.0 for academic engagement, academic achievement, and career adaptability. First, unconditional linear LGMs were specified for each construct across the four time points to test whether they exhibited linear growth over the two-year period. Each model included two latent factors: the intercept (Int), representing the initial level, and the slope (Slp), representing the rate of change. Factor loadings for the intercept were fixed at 1, while slope loadings were fixed at 0 (T1), 1 (T2), 2 (T3), and 3 (T4). Second, to further explore grade-level differences in the developmental trajectory of career adaptability, a conditional LGM was estimated with grade as a predictor. Finally, after establishing the growth trajectories of academic engagement, academic achievement, and career adaptability, a latent growth mode was constructed to test whether trajectories of academic engagement and academic achievement predicted the trajectory of career adaptability. Model fit was evaluated using the following criteria: CFI > .96, TLI > .96, RMSEA < .07, and SRMR < .08 (Hair et al., 2019).

Results

Descriptive Statistics

Descriptive statistics and correlation analyses for each variable are presented in Table 1. Except for a few time points, significant positive correlations were observed among the variables.

Table 1.

Descriptive Statistics and Bivariate Correlation Coefficients for Research Variables

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
1. Major academic engagement T1	-
2. Major academic engagement T2	.47^**	-
3. Major academic engagement T3	.46^**	.47^**	-
4. Major academic engagement T4	.42^**	.45^**	.54^**	-
5. Non-major academic engagement T1	.89^**	.42^**	.43^**	.37^**	-
6. Non-major academic engagement T2	.39^**	.90^**	.44^**	.39^**	.42^**	-
7. Non-major academic engagement T3	.43^**	.43^**	.94^**	.51^**	.42^**	.45^**	-
8. Non-major academic engagement T4	.38^**	.42^**	.53^**	.95^**	.37^**	.41^**	.52^**	-
9. Major academic achievement T1	.05	.07^*	.07^*	.09^**	.01	.04	.05	.09^**	-
10. Major academic achievement T2	.05	.09^**	.09^**	.14^**	.02	.07^*	.07^**	.13^**	.76^**	-
11. Major academic achievement T3	.02	.07^*	.05	.08^**	.01	.05	.03	.08^**	.61^**	.63^**	-
12. Major academic achievement T4	.03	.09^**	.09^**	.11^**	.01	.06^*	.07^*	.10^**	.57^**	.57^**	.61^**	-
13. Non-major academic achievement T1	.04	.04	.05	.08^**	.04	.05	.03	.08^**	.39^**	.38^**	.37^**	.32^**	-
14. Non-major academic achievement T2	.03	.08^**	.06^*	.07^**	.04	.08^**	.06^*	.08^**	.45^**	.48^**	.39^**	.38^**	.28^**	-
15. Non-major academic achievement T3	.04	.04	.09^**	.07^*	.09^**	.07^*	.09^**	.09^**	.48^**	.45^**	.37^**	.42^**	.12^**	.47^**	-
16. Career adaptability T1	.56^**	.30^**	.37^**	.33^**	.52^**	.25^**	.35^**	.32^**	.05	.03	.02	.04	.09^**	.03	.00	-
17. Career adaptability T2	.35^**	.65^**	.40^**	.38^**	.32^**	.59^**	.39^**	.37^**	.07^*	.09^**	.08^**	.06	.10^**	.09^**	.03	.41^**	-
18. Career adaptability T3	.37^**	.38^**	.73^**	.47^**	.36^**	.36^**	.70^**	.47^**	.10^**	.11^**	.12^**	.15^**	.10^**	.09^**	.13^**	.45^**	.47^**	-
19. Career adaptability T4	.33^**	.35^**	.49^**	.71^**	.29^**	.32^**	.45^**	.69^**	.07^*	.10^**	.10^**	.10^**	.09^**	.05	.06^*	.38^**	.42^**	.57^**	-
M	78.75	79.29	80.44	80.01	75.17	76.50	79.34	78.97	-	-	-	-	-	-	-	93.02	89.57	89.24	88.53
SD	20.12	20.23	20.09	20.61	20.67	20.79	20.32	20.66	-	-	-	-	-	-	-	18.43	19.75	20.04	20.21

Notes. Academic achievement is a Z-score. At time point T4, the non-major courses of 553 subjects had been completed. Therefore, the data of non-major academic achievement were only collected at time points T1 to T3. ^*p < 0.01, ^**p < 0.001.

Developmental Trajectories of Academic Engagement, Academic Achievement, and Career Adaptability

Unconditional linear latent growth models (LGM) were constructed using Mplus 8.0 to explore the developmental trajectories of academic engagement, academic achievement, and career adaptability. The model fit indices, as well as the means of the intercepts and slopes, are presented in Table 2. The results showed that both major and non-major academic engagement models fit well, with positive slope means for both variables (M _major = 0.48, p < 0.05; M _non-major = 1.56, p < 0.001), indicating that both major and non-major academic engagement increased significantly over time. Additionally, the correlations between the intercepts and slopes for both major and non-major academic engagement were negative, suggesting that higher initial levels of engagement were associated with slower rates of increase.

Table 2.

Fitting Index of Latent Variable Growth Model

	χ² (df)	RMSEA	CFI	TLI	SRMR	M		R
	χ² (df)	RMSEA	CFI	TLI	SRMR	INT	SLP	R
Major academic engagement	5.53 (5)	0.01	1.00	0.99	0.02	78.75^***	0.48^*	−10.03^*
Non-major academic engagement	11.06 (5)	0.03	0.99	0.99	0.02	75.16^***	1.56^***	−14.19^*
Major academic achievement	27.23 (5)	0.06	0.99	0.99	0.02	0.00	0.00	−0.09^***
Non-major academic achievement	1.38 (1)	0.02	0.99	0.99	0.01	0.03	−0.09^***	−0.16^***
Career adaptability	33.25 (5)	0.07	0.97	0.97	0.05	91.86^***	−1.00^***	−3.31

The models for major and non-major academic achievement fit well. The slope mean for major academic achievement was 0 (M = 0.00, p > 0.05), indicating that major academic achievement remained stable over the 2-year period. The correlation between the intercept and slope for major academic achievement was negative, suggesting that higher initial levels of major academic achievement were associated with slower rates of increase. In contrast, the slope mean for non-major academic achievement was negative (M = −0.09, p < 0.001), indicating a declining trend in non-major academic achievement. Furthermore, the correlation between the intercept and slope for non-major academic achievement was significantly negative, suggesting that higher initial levels of non-major academic achievement were associated with faster rates of decline.

The model for career adaptability also fit well, with a negative slope mean (M = −1.00, p < 0.001), indicating a significant decline in career adaptability over time. However, the correlation between the intercept and slope for career adaptability was not significant, suggesting that the initial level of career adaptability was not significantly related to its rate of change.

Additionally, to further examine grade-level differences in the developmental trajectory of university students’ career adaptability, a conditional latent growth model was constructed by adding grade as a covariate to the unconditional linear model. The model demonstrated a good fit to the data, χ² (7) = 33.76, RMSEA = 0.05, CFI = 0.98, TLI = 0.97, SRMR = 0.04. The regression coefficients of grade on the intercept and slope were −2.99 (p < 0.01) and 1.31 (p < 0.01), respectively, indicating that grade significantly predicted both the initial level and the rate of change in career adaptability. Specifically, compared to first-year students, second-year students showed a lower initial level of career adaptability (intercept), while their decline over time (slope) was less steep, suggesting a more gradual downward trend.

The Impact of Changes in Academic Engagement on Changes in Career Adaptability

To examine the impact of changes in academic engagement on changes in career adaptability, latent growth models were constructed to assess the effects of changes in major and non-major academic engagement on changes in career adaptability (see Figure 1). The results showed that the models examining the impact of changes in major and non-major academic engagement on changes in career adaptability fit well, χ² (22) = 817.21, RMSEA = 0.03, CFI = 0.96, TLI = 0.96, SRMR = 0.03; χ² (22) = 778.92, RMSEA = 0.04, CFI = 0.95, TLI = 0.96, SRMR = 0.04. The initial levels (intercepts) of major and non-major academic engagement had significant positive predictive effects on the initial level (intercept) of career adaptability (β _major = 0.91, p < 0.001; β_non-major = 0.85, p < 0.001). However, the initial levels (intercepts) of major and non-major academic engagement did not significantly predict the rate of change (slope) in career adaptability (β_major = −0.01, p > 0.05; β _non-major = 0.04, p > 0.05). In contrast, the rates of change (slope) in major and non-major academic engagement had significant positive predictive effects on the rate of change (slope) in career adaptability (β_major = 2.83, p < 0.001; β_non-major = 1.96, p < 0.001).

The Impact of Changes in Academic Achievement on Changes in Career Adaptability

To examine the impact of changes in academic achievement on changes in career adaptability, latent growth models were constructed to assess the effects of changes in major and non-major academic achievement on changes in career adaptability (see Figure 2, Figure 3). The model examining the impact of changes in non-major academic achievement on changes in career adaptability was based on data from T1–T3.

The results showed that the model examining the impact of changes in major academic achievement on changes in career adaptability fit well, χ² (22) = 70.30, RMSEA = 0.04, CFI = 0.99, TLI = 0.98, SRMR = 0.03. The initial level (intercept) of major academic achievement had a marginally significant positive predictive effect on the initial level (intercept) of career adaptability (β = 1.26, p = 0.07). The initial level (intercept) of major academic achievement had a significant positive predictive effect on the rate of change (slope) in career adaptability (β = 0.96, p < 0.01). Additionally, the rate of change (slope) in major academic achievement had a significant positive predictive effect on the rate of change (slope) in career adaptability (β = 6.14, p < 0.01).

The model examining the impact of changes in non-major academic achievement on changes in career adaptability also fit well, χ² (7) = 19.43, RMSEA = 0.04, CFI = 0.99, TLI = 0.98, SRMR = 0.02. The initial level (intercept) of non-major academic achievement had a significant positive predictive effect on the initial level (intercept) of career adaptability (β = 3.99, p < 0.01). However, the initial level (intercept) of non-major academic achievement did not significantly predict the rate of change (slope) in career adaptability (β = 1.18, p > 0.05). In contrast, the rate of change (slope) in non-major academic achievement had a significant positive predictive effect on the rate of change (slope) in career adaptability (β = 3.66, p < 0.001).

Discussion

Grounded in the career construction model of adaptation, this study examined the developmental trajectories of career adaptability, academic engagement, and academic achievement among university students, with particular attention to the predictive effects of academic factors on changes in career adaptability.

The findings revealed a consistent decline in career adaptability among both first- and second-year students over 2 years. Interestingly, second-year students reported lower initial levels of career adaptability than first-year students, yet their decline was less steep. This pattern did not support Hypothesis 1 and diverges from some prior research (Autin et al., 2017; Fu et al., 2023). One possible explanation lies in the contextual influence of the COVID-19 pandemic. During this period, recruitment demand contracted sharply, and many students opted to postpone workforce entry by preparing for graduate school entrance examinations or civil service recruitment. Such postponement may have reduced their overall career adaptability (Li et al., 2022). Furthermore, the widespread shift to online learning limited opportunities for practice-oriented experiences, constraining the development of competencies crucial for the school-to-work transition (Peng et al., 2022). Collectively, these contextual disruptions may account for the downward trajectory of career adaptability observed in this study.

With respect to academic engagement, both major- and non-major-related engagement increased significantly over time. Contrary to Hypothesis 2, this suggests that students gradually recognized the differential value of major and non-major coursework for their career development and accordingly invested more time and effort. However, trends in academic achievement diverged: major academic achievement remained stable, while non-major achievement declined, thereby confirming Hypothesis 3. A plausible explanation is that major academic achievement reflects the gradual accumulation of domain-specific knowledge and skills, which are less subject to short-term fluctuations (McCoach et al., 2017). In contrast, non-major courses—often general education subjects—emphasize rapid knowledge acquisition rather than sustained professional skill-building (Barnett, 2004). Additionally, as students progress through university, the increasing difficulty and workload of major courses may lead them to prioritize major-related studies to strengthen their professional competitiveness (Cai, 2019), which in turn reduces investment in non-major courses and results in declining performance.

More critically, both major and non-major academic engagement emerged as significant positive predictors of career adaptability, consistent with previous cross-sectional evidence (Datu & Buenconsejo, 2021). Moreover, upward trajectories of engagement helped mitigate the decline in career adaptability, thereby supporting Hypothesis 4. These findings suggest that students with higher academic engagement not only demonstrate initiative and adaptive motivation in their studies but also cultivate openness and resilience in response to environmental changes (Martínez et al., 2019; Tolentino et al., 2013). This provides longitudinal support for the theoretical proposition that academic engagement, as a form of adaptive readiness, facilitates the development of career adaptability (Johnston, 2018; Šverko & Babarović, 2019).

In terms of academic achievement, results revealed nuanced effects. The initial level of major academic achievement not only predicted immediate career adaptability but also exerted a sustained influence on its long-term trajectory. In contrast, the initial level of non-major achievement predicted only immediate adaptability but had no long-term effect. Nevertheless, upward trends in both major and non-major academic achievement positively predicted the developmental trajectory of career adaptability, thus supporting Hypothesis 5. This highlights the stronger role of major academic achievement in shaping career adaptability. One explanation is that success in major coursework enhances students’ professional knowledge, strengthens career interests, and facilitates alignment with future occupations, thereby easing the school-to-work transition (Vuolo et al., 2013). Students with higher academic achievement also tend to hold more positive career expectations and exhibit a greater sense of control over their professional development, further promoting career adaptability (Li & Liu, 2022). Overall, these findings reinforce the notion that academic achievement, as adaptive readiness, is a critical driver of career adaptability (Johnston, 2018; Šverko & Babarović, 2019).

At the theoretical level, this study contributes to the career construction model by integrating both process variables (academic engagement) and outcome variables (academic achievement) into the analysis of university learning, while also distinguishing between major and non-major coursework. By examining their longitudinal relationships with career adaptability, the study expands the conceptualization of adaptive readiness and offers empirical evidence that readiness in academic contexts can translate into adaptive resources for career development.

At the practical level, the findings provide meaningful implications for higher education. Specifically, interventions aimed at fostering career adaptability should not be limited to direct training in career-related skills. Instead, enhancing students’ academic engagement and achievement may serve as effective indirect strategies to simultaneously strengthen adaptability. In this sense, embedding career education into everyday academic practices can enable students to acquire robust professional competencies while cultivating adaptability for future career transitions. Such integration not only meets the developmental needs of university students but also advances the quality of career education at the institutional level, offering actionable guidance for universities seeking to better prepare students for the evolving labor market.

Limitations and Future Research Directions

This study is not without limitations, which provide opportunities for future research. First, with regard to participant selection, the sample was limited to students from two provincial universities in Shandong Province. This relatively homogeneous sample constrains the generalizability of the findings to broader populations of university students. Future studies should expand sampling to include universities of different tiers and regions across China and consider additional contextual factors such as institutional type, family socioeconomic background, and urban–rural disparities. Such efforts would allow for a more comprehensive understanding of the interplay between academic experiences and career adaptability.

Second, concerning research design, although this study followed participants over two years and collected data at four time points, the six-month interval between surveys may not have been optimal for capturing all variables. While such intervals are adequate for relatively stable constructs such as academic achievement, they may be too coarse for dynamic constructs such as academic engagement, which could fluctuate over shorter periods. Future research could employ shorter intervals or intensive longitudinal designs to more precisely capture variability in engagement and its influence on career adaptability.

Third, in terms of research scope, this study did not simultaneously examine all four predictors—major and non-major academic engagement, and major and non-major academic achievement—within a single model. The main reason was that including all predictors yielded an overly complex model with poor fit. Although analyzing separate models limits the ability to directly compare the relative contributions of each variable and may obscure potential interactions, Savickas (2013) highlighted the importance of distinguishing between “institutional commitment” and “personal meaning-making” when evaluating the educational antecedents of career adaptability. This theoretical perspective informed our decision to separately analyze major and non-major academic factors. Nonetheless, future research could employ approaches such as cross-lagged panel models or integrative structural equation modeling to more comprehensively test their combined and interactive effects.

Conclusion

This study found that initial levels and developmental trajectories in academic engagement and academic achievement positively influence the initial levels and developmental trajectories in career adaptability. These findings provide longitudinal support for career construction theory from the academic domain, underscoring the role of educational experiences as adaptive readiness factors that shape career adaptability over time. The results suggest that fostering students’ career adaptability does not necessarily require isolated career interventions; rather, it can be promoted indirectly by enhancing students’ engagement and achievement within their academic pursuits. By integrating career education into everyday learning tasks and strengthening students’ academic development, universities can simultaneously equip students with professional competencies and adaptive capacities for future career transitions. This integrated approach not only advances theoretical understanding but also offers actionable guidance for embedding career education into the academic mission of higher education institutions.

Footnotes

ORCID iDs

Xia Su

Ping Li

Zhenhua Dong

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

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