Enhancing Career Adaptability Among University Students: An Intervention Study

Abstract

Recent college graduates are navigating a complex labor market due to the COVID-19 pandemic, changing economic conditions, and advancing technologies. Career adaptability, a psychosocial construct focused on managing career transitions, is critical for college students in this environment. Career adaptability interventions have shown promising results, but many are time consuming or involve one-on-one counseling, and none have focused on US college samples, which prompted this study. We tested a brief career adaptability training on a sample of 89 US college students and measured its effect on participants’ career adaptability resources (career concern, career control, career curiosity, career confidence) and career adapting responses (career decision self-efficacy, career planning). Results showed no increase in career adaptability resources or career planning but indicated an increase in career decision self-efficacy, suggesting that brief interventions may be effective for some desired outcomes but not others. Future research should examine which intervention ingredients are necessary to enhance career adaptability.

Keywords

career adaptability career decision self-efficacy school-to-work transition career intervention

Introduction

In recent years, new college graduates have faced a tough labor market, likely attributable to disruption from the COVID-19 pandemic and fluctuating economic conditions. The unemployment rate for college graduates was at its highest in a decade at 13.7% in June 2020, and since the start of 2021, the unemployment rate for those aged 22–27 with a bachelor’s degree or higher has surpassed the national average every month (The Federal Reserve Bank of New York, 2022). Although the effects of the COVID-19 pandemic seem to have subsided, new college graduates are still struggling to make career decisions and obtain employment in an ever-changing world of work.

On one hand, career choices for college students are copious. In 2010, U.S. colleges and universities provided close to 1,500 academic programs to students (Simon, 2012). Yet, when students are ready to enter the workforce, they find it difficult to obtain jobs that fit their interests and qualifications. One likely barrier is that entry-level positions are not easy to acquire. In a recent analysis of close to 4 million jobs from 2017 to 2021 on LinkedIn, 35% of postings for “entry-level” positions asked for multiple years of prior relevant experience, a qualification that new graduates rarely have (Anders, 2021). Additionally, when students do obtain employment, job mismatches or underemployment are common. Although it may be assumed that having a job one is overqualified for or uninterested in is better than having no job, underemployment is linked to a broad range of negative outcomes such as resignation or turnover intentions (Erdogan & Bauer, 2009; McKee-Ryan & Harvey, 2011). In addition to marketplace-related factors, career theory has long acknowledged the influence that one’s personal characteristics and context have on their ability to gain employment. For example, the psychology of working theory (PWT; Duffy et al., 2016) identifies psychosocial factors and contextual factors that lead to one’s ability to obtain decent work. Specifically, PWT suggests that economic constraints and marginalization are key predictors of one’s ability to secure decent work, and one’s career adaptability and work volition play a mediating role in this relationship. Importantly for this study, career adaptability is a psychosocial construct that refers to resources that individuals need to successfully manage current and anticipated career transitions. According to PWT, an individual affected by higher economic constraints and marginalization but who also has high career adaptability may still obtain decent work (Duffy et al., 2019).

Career Adaptability

Savickas (2005) suggested that career adaptability was comprised of four resources, including career concern, career control, career curiosity, and career confidence. Concern refers to one’s ability to care about and plan for the future. Control is the inclination to think of one’s future as manageable despite challenges. Curiosity is exploring one’s career options and thinking about the fit between the self and the work environment. Confidence relates to one’s perceived ability to face and address career challenges. Career adaptability highlights individuals’ readiness to cope with both predictable and unpredictable challenges for tasks and roles in continually changing work environments (Savickas & Porfeli, 2012).

Given the difficulty of the school-to-work transition (Anders, 2021), it would be advantageous to provide college students with the resources to cope (i.e., enhance their career adaptability). Indeed, there are positive associations between career adaptability and outcomes including employability (de Guzman & Choi, 2013) and career satisfaction (Chan & Mai, 2015), and negative associations between career adaptability and perceptions of internal and external barriers (Soresi et al., 2012).

Career Adaptability Interventions

Career adaptability intervention studies have found encouraging results, but the length and intensity of career adaptability interventions has varied (Johnston, 2018), and research on whether those factors affect outcomes is inconclusive (van der Horst et al., 2021). Many existing interventions are designed to be conducted over several weeks or months (e.g., Cheung & Jin, 2016; Green et al., 2020), or involve one-on-one counseling (e.g., Scholl & Cascone, 2010), which require substantial resources in terms of time and staff and may not be an ideal solution for universities with limited budgets who are aiming to help thousands of students reach their career goals. Additionally, few career adaptability interventions have focused on the developmental stage (college) and country context (United States [US]) of interest in this study. College students differ from middle and high school students in that they typically have been exposed to career development topics, have more relevant experience, and career preparation is highly salient. Working adults differ from college students in that their career adaptability foci extend beyond the school to work transition (Savickas, 2013). Country and cultural factors also present different demands (Savickas & Porfeli, 2012). Several countries where brief career adaptability interventions have been tested including Hong Kong (Cheung & Jin, 2016), China (Gai et al., 2022) and the Netherlands (Koen et al., 2012; van der Horst et al., 2021) rank much higher than the US on long-term orientation, suggesting that they can adapt traditions more easily to changing conditions. The difference is evidenced by the fact that American businesses measure performance on a short-term basis (Hofstede et al., 2010), which may have implications for career adaptability interventions. For example, activities focused on short-term goals and scenarios applicable to current market conditions will likely be more effective.

The Present Study

We aimed to test a brief training intervention focused on enhancing career adaptability among college students. We anticipated that support for such an intervention would make a substantial contribution to the literature. For example, it would reinforce the usefulness of interventions in changing career adaptability from pre to posttest, a goal for which Johnston (2018) found only three studies, and it would provide evidence for the feasibility and effectiveness of a brief intervention that could be used in university settings. It would also fill a gap in the literature by testing a career adaptability intervention on a previously unexamined population (US college students). Lastly, if supported, evaluating the effects of a career adaptability intervention could help guide future research into the potential mechanisms of change in career adaptability, as well as potential moderators of intervention effects, both of which would have implications for universities, career practitioners, and college students in helping students thrive in the school-to-work transition and beyond.

We expected the intervention would increase career adaptability resources (career concern, career control, career curiosity, career confidence) and career adapting responses (career planning, career decision self-efficacy) through psychoeducation and by generating reflection and reflexivity. Career construction theory (CCT; Savickas, 2013) introduced reflection and reflexivity as mechanisms for change; reflection refers to recalling the past and learning about oneself, and reflexivity adds agency to self-awareness by using the knowledge one has gained to plan for the future. Our training was designed around each career adaptability resource and set out to 1) encourage self-reflection on each factor, and 2) provide an opportunity to conceptualize knowledge for future action.

Studies have supported the efficacy of reflexivity for building career adaptability (e.g., Bimrose et al., 2019) and Bimrose et al. (2019) recommended that interventions include components such as learning in a community and the ability to contextualize individual situations in open discussions with peers. CCT also theorizes that building career adaptability leads to adapting behaviors, however, recent research has suggested that the relationship is general instead of linking one-to-one as originally theorized (Hirschi et al., 2015). Nonetheless, we expected that the intervention may increase participants’ career planning and career decision self-efficacy, both of which enable students to meet today’s job market demands and specifically the transition from school to work (Neuenschwander & Garrett, 2008; Savickas et al., 2018). We chose to measure only two responses to increase focus and reduce participant burden given the two data collection periods were only 1 week apart. Career decision-making self-efficacy and career planning were chosen due to the strong empirical support for their association with positive job search and employment outcomes (Choi et al., 2012; Zikic & Klehe, 2006). Career decision-making self-efficacy refers to the extent to which people believe they can effectively make career decisions related to interests, goals, opportunities, and barriers (Taylor & Betz, 1983). Career planning refers to the ability to think about, plan, and take steps toward reaching career goals (May, 2005).

The following hypotheses were tested:

Hypothesis 1: The intervention group would have a significantly higher level of career concern, career control, career curiosity, and career confidence (career adaptability) than the control group, after controlling for pretest levels of career adaptability.

Hypothesis 2: The intervention group would have a significantly higher level of career decision self-efficacy than the control group, after controlling for pretest levels of career decision self-efficacy.

Hypothesis 3: The intervention group would have a significantly higher level of career planning than the control group, after controlling for pretest levels of career planning.

Method

Participants

Participants in the study were undergraduate students attending a large public university in the Southeastern US. They were recruited from a research pool and an undergraduate class in two semesters: Spring 2022 and Summer 2022. A total of 89 students enrolled. The inclusion criteria required that participants were adults attending college. The exclusion criteria required that participants did not already have a job secured for after college, because we reasoned those who already secured a job may already possess strong career adaptability resources and would be less likely to benefit from an intervention. Participants were randomly assigned to either the intervention or wait-list control group. In the first data collection wave, 58 participants enrolled and were randomly assigned, but 17 dropped out between the Time 1 to Time 2 data collections. In the second wave, 31 participants enrolled and were randomly assigned, and 3 participants dropped out by Time 2. The total sample included 62 women (70%) and 27 men (30%). Ages ranged from 19 to 50 years (M = 23.76, SD = 5.34). Thirty-nine (43.3%) participants were in their fourth year of college, 25 (27.8%) were in their third year, 13 (14.4%) were in their second year, 11 (12.2%) were in their fifth year or more, and 1 student (1.1%) was in their first year. Forty-five (50.1%) were Black, African, or African American, 17 (19.1%) were white, 14 (15.7%) were Asian or East Asian Indian or Central Asian, 3 (3.4%) were Arabic or Middle Eastern, and the remaining 10 (11.2%) of were other or mixed race.

Measures

Career Adapt-Abilities Scale-USA Form (CAAS; Savickas & Porfeli, 2012)

We used the 24-item CAAS at Time 1 (pretest) and Time 2 (posttest) to assess career adaptability. Participants rated strengths of their career adaptabilities using a scale of 1 = not strong, 2 = somewhat strong, 3 = strong, 4 = very strong, 5 = strongest). Example items were, “realizing that today’s choices shape my future” (concern), “counting on myself” (control), “becoming curious about new opportunities” (curiosity), and “working up to my ability” (confidence). Based on a sample of 460 US high school students, Savickas and Porfeli (2012) reported strong internal consistency estimates ranging from .80 to .90 for the four scores. CAAS scores have shown good reliability and validity in several studies of college students (e.g., Douglass & Duffy, 2015). In the current study, Omega ω was .87 for concern, .86 for control, .88 for curiosity, and .86 for confidence.

Thinking and Planning Subscale of the Career Salience Scale (Greenhaus, 1971)

Career planning was measured with the 8-item Thinking and Planning subscale. Item response options were 1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, and 5 = strongly agree. Example items include “I enjoy thinking about and making plans about my future career” and “I don’t think too much about what type of job I’ll be in ten years from now.” The original scale was tested on students from two eastern colleges in the US and evidenced good internal consistency (α = .81), and a more recent study using a college student sample reported Cronbach’s α of .87 (Green et al., 2020). In the current study Omega ω was .64.

Career Decision-Making Self-Efficacy (CDSE-SF; Betz & Voyten, 1997)

We used the 25-item CDSE-SF at Time 1 (pretest) and Time 2 (posttest) to assess confidence in ability to complete career-decision making tasks, including engaging in accurate self-appraisal, obtaining the accurate occupational information, engaging in goal selection, planning for the future, and engaging in problem-solving. Participants were asked to rate the items using a 5-point scale ranging from 1 (no confidence at all) to 5 (complete confidence). The developers tested the scale on college students at a US midwestern university and found a Cronbach’s α of .93. In the current study, Omega ω was .95.

Procedures

Before conducting the study, university IRB approval was obtained. We informed students about the study by posting an announcement on the research participation pool website. Additionally, we requested that a professor distribute the announcement to students in a large undergraduate course. Once participants expressed interest, they were given more information and informed consent. Participants who signed up through the research participation pool were given up to three research credits for their participation in the study, and students who signed up through the undergraduate course were given a minor number of bonus points. Participants who were assigned to the wait-list control group were informed that their workshop sessions would be slightly delayed, and participants assigned to the intervention group were informed when and where they would need to show up for the study. The intervention group attended two workshop sessions scheduled 1 week apart in a classroom on the university’s campus: each lasted 1.5 hours. Workshop sessions were led by a graduate student researcher with a professional background and education in the field of human and organizational development. Both the intervention and control groups completed questionnaires with the study measures before the first intervention workshop, and immediately following the second workshop. They were required to complete the questionnaires within 24 hours. The wait-list control group was debriefed on the study and offered an optional, 1.5-hour workshop following data collection.

Training

The training was informed by career construction theory (Savickas, 2005; 2013). It sought to enhance career adaptability by training participants on each resource (concern, curiosity, control, confidence) and fostering reflection and reflexivity to produce change.

Content

The training consisted of two, hour-and-a-half sessions (see Table 1) that aimed to increase participants career adaptability resources across the four dimensions. Session 1 focused on introducing career adaptability and building concern and curiosity. Concern was addressed through a visualization activity where participants imagined and shared their future career and then completed a career planning worksheet to take stock of their progress and yet to be completed tasks. The activities combined reflection and reflexivity as they encouraged participants to reflect on what they have accomplished and contextualize it in relation to the future careers they imagine. Another activity aimed to generate curiosity by giving participants access to an online tool to explore a career path of their choice and a worksheet to reflect on how the career path fit with what they know about the self. Session 2 aimed to build career confidence and control and summarize and instill learnings. Participants crafted elevator pitches and practiced among peers. The process of crafting a pitch not only attempted to build participants’ confidence but it also intended to help them bring past self-reflections together with future career goals. Next, in a series of activities that targeted control and confidence, participants were grouped together and asked to respond to example career challenge scenarios relevant to the current labor market. Then, participants identified barriers in their own career journeys, and with the help of their peers and other print and online resources, they generated creative solutions to apply in their own lives. In response to Bimrose et al.’s (2019) recommendations, the intent was to promote an interactive learning community to allow the interplay of reflection and reflexivity to play out in connection with peers.

Table 1.

Content of the Training.

Session #	Objective	Dimension(s)	Representative Activities
1	Define career adaptability and its four dimensions (concern, curiosity, confidence, control), explain its significance as a personal resource	Concern	Psychoeducation session and group discussion
1	Reflect on and apply concern dimension to individual career path	Concern	Career visualization activity Career planning worksheet The Fantasy, The Ideal, and The Reality of Career Exploration video
1	Reflect on and apply curiosity dimension to individual career path	Curiosity	Career exploration activity—“What Can I Do With This Major?” and ONet
2	Reflect on and apply confidence dimension to individual career path	Confidence	Elevator pitch activity
2	Recommend strategies for overcoming career obstacles or managing career demands	Confidence Control	Scenario activity
2	Reflect on and apply control dimension to individual career path	Control	Identifying barriers activity Creative solutioning activity

Structure and Process

The training was guided by Knowles’ (1990) five adult learning assumptions: 1) self-directedness, 2) importance of previous experience, 3) readiness to learn, 4) orientation to learning, and 5) motivation to learn. Self-directedness refers to an adult’s maturing self-concept and a desire for autonomy. In our intervention, participants could choose from several online activities to explore a career path and compare it to their values and expectations. Knowles (1990) also believed that an adult’s accumulated life experiences are a resource for learning. Instead of utilizing lectures or readings, we incorporated group discussions, scenarios, and activities that encouraged participants to draw from their experiences to deepen their and others’ learning. Readiness to learn refers to the idea that adults want their learning experiences to relate to the developmental phase they are in or the roles they occupy. The intervention was tailored to raise questions and challenges related to the school-to-work transition, which is a developmental phase unique to college students. Additionally, since participants varied across grade levels and were likely at different stages in their career decision-making journeys, we offered different activities to accommodate participants who were A) confident in their career decisions or B) still determining a career path. Lastly, adults’ orientation to learning is a problem-centered approach, which means they desire to apply what they are learning right away. The intervention provided students with the opportunity to take stock of steps they had already taken towards their career and highlight critical gaps and next steps through a worksheet activity. Several critical components from Brown et al. (2003) were incorporated including written exercises, world of work information, modeling, and attention to building support. The only component not included was individualized feedback due to the brief format of the training.

Results

Preliminary Analyses

Table 2 displays the sample means and standard deviations for study variables, and Table 3 displays reliabilities and correlations. Scores were calculated based on average item responses. Pretest averages were consistent with other college student samples (e.g., Green et al., 2020). To assess whether the intervention group and control group would be comparable before the intervention, we examined whether the two groups differed significantly on demographics, the four career adaptability dimensions, or the career adapting responses at Time 1. Results indicated no statistically significant differences between the groups on age t(87) = 1.46, p = .147, binary sex χ2 (1,89) = 1.50, p = .221), year in school t(87) = 1.08 p = .283, socioeconomic status t(87) = −0.36, p = .720, career concern t(87) = −0.08, p = .939, career control t(87) = 0.84, p = .405, career curiosity t(87) = −0.01, p = .991, or career confidence t(87) = 0.50, p = .620. Additionally, no significant differences were found between the two groups on career planning t(87) = 0.45, p = .651, or career decision self-efficacy t(87) = −0.86, p = .395. Omega ω reliabilities were similar and high for the intervention and control group on concern, control, curiosity, confidence, and career self-efficacy, ranging from .84 to .95. Career planning was the exception which was .68 for the intervention group and .61 for the control group. The attrition rate for the overall sample was 22%, so we also examined whether there were significant differences in study variables for participants who dropped out of the study after Time 1 versus participants who completed the full study. Results indicated no significant differences between the attrition versus non-attrition groups for career concern t(87) = −0.80, p = .424, career control, t(87) = 0.28, p = .784, career curiosity t(87) = −0.15, p = .878, career confidence t(87) = −0.15, p = .879, career self-efficacy t(87) = 0.51, p = .613, or career planning t(87) = −0.93, p = .355. Results also indicated there were no differences in age t(87) = 1.11, p = .271, binary sex χ2 (1,89) = 2.87, p = .090, or socioeconomic status t(87) = 1.54, p = .127. Because no significant differences were found, it can be concluded that the item nonresponses for those who dropped out of the study were likely missing at random (Rubin, 1976).

Table 2.

Means and Standard Deviations Among Study Variables and Effects of the Intervention on Career Adaptability Resources and Responses.

	Training				Control
	Time	M	SD	M	SD	F (2,67)	p	$η_{p}^{2}$
Career adaptability resources
Career concern	Time 1	3.64	.85	3.65	.92
	Time 2	3.94	.72	3.69	.95	2.07	.155	0.03
Career control	Time 1	3.65	.77	3.50	.89
	Time 2	3.78	.78	3.64	.92	0.14	.707	0.00
Career curiosity	Time 1	3.59	.92	3.59	.84
	Time 2	3.85	.78	3.68	.82	1.99	.163	0.03
Career confidence	Time 1	3.63	.80	3.54	.79
	Time 2	3.77	.86	3.65	.87	0.15	.701	0.00
	Training				Control
	Time	M	SD	M	SD	F (2,67)	p	$η_{p}^{2}$
Career adapting responses
Career decision self-efficacy	Time 1	3.64	.75	3.78	.72
	Time 2	3.91	.59	3.68	.78	7.84	.007	0.01
Career planning	Time 1	3.28	.39	3.24	.36
	Time 2	3.27	.46	3.36	.29	1.16	.285	0.02

Note. Training group N = 45, Control group N = 44.

Table 3.

Correlations and Reliabilities Among Study Variables.

Variable	1	2	3	4
Career adaptability resources
1. Career concern	—	.74*	.75*	.69*
2. Career control	.73*	—	.80*	.86*
3. Career curiosity	.80*	.76*	—	.83*
4. Career confidence	.75*	.84*	.83*	—
	1	2
Career adapting responses
1. Career decision self-efficacy	—	.42*	—	—
2. Career planning	.39*	—	—	—

Note. Correlations for the training group (n = 45) are displayed above the diagonal; correlations for the control group (n = 44) are displayed below the diagonal. *p < .01 (two-tailed).

Next, the data were examined to determine whether the assumptions for the Analysis of Covariance (ANCOVA) analysis were met. First, no outliers were found for any variables. Next, the assumption of normality was tested, which all variables met except for career concern. For career concern, a Shapiro–Wilk test showed a significant departure from normality for the control group W (36) = .93, p = .022. However, ANCOVAs are robust to violations of normality (Levy, 1980; Harwell, 2003), especially when group sizes are balanced, which was true of our study. Next, all variables met the equality of variance assumption according to Levene’s test of equal variances, ps > .098. Thus, we continued with our main analyses.

Hypothesis Testing

We predicted that the career adaptability intervention would positively influence measures of career adaptability resources (Hypothesis 1), and career adapting responses (Hypothesis 2 and 3). To test the hypotheses, we conducted several ANCOVAs with group as the fixed factor, the Time 1 variable as the covariate, and the Time 2 variable as the dependent variable. The effect size was assessed using partial eta-squared ( $η_{p}^{2})$ which estimates the percentage of variance explained by each variable.

Results of ANCOVAs for hypothesis 1 (Table 2) revealed that there was not a statistically significant difference between the intervention group and control group on measures of career adaptability at Time 2, controlling for Time 1 measures, indicating that hypothesis 1 was not supported. Specifically, results indicated that there was not a significant difference between the intervention and control group for career concern, F(2, 67) = 2.07; p = .155; $η_{p}^{2}$ = 0.03, nor was there a difference for career control, F(2, 67) = 0.14; p = .707 $η_{p}^{2} = 0.00$ , career curiosity, F(2, 67) = 1.99, p = .163, $η_{p}^{2} = 0.03$ , or career confidence, F(2, 67) = 0.15; p = .701, $η_{p}^{2} = 0.00$ .

Results of the ANCOVA for hypothesis 2 (Table 2) indicated that there was a statistically significant difference between the intervention group and control group for career decision self-efficacy at Time 2, controlling for Time 1, F(2, 67) = 7.84; p = .007, $η_{p}^{2} = 0.01$ , signifying that hypothesis 2 was supported. The direction of effects for the average scores suggested that the intervention had a positive impact on participants’ career decision self-efficacy (see Table 2).

Lastly, results of the ANCOVA for hypothesis 3 (Table 2) revealed that there was not a statistically significant difference between the intervention and control group for career planning at Time 2, controlling for Time 1, F(2, 67) = 1.16; p = .285, $η_{p}^{2} = 0.02$ . This indicates that hypothesis 3 was not supported.

Discussion

Navigating the school-to-work transition is a complex challenge for college students in today’s work environment. Career adaptability resources, or coping strategies that can help individuals manage career related challenges, have been linked to positive outcomes such as employability and career satisfaction (de Guzman & Choi, 2013; Chan & Mai, 2015). Career adaptability is malleable, making it ripe for intervention, and although several career adaptability intervention studies have had promising results, many have used time-consuming trainings that may not be ideal for universities, and few have focused on US college students. In this study, we wanted to determine if a brief workshop focused on enhancing career adaptability would produce desired outcomes.

For the first hypothesis, results revealed that the intervention group did not have significantly higher levels of career adaptability (career concern, career control, career curiosity, and career confidence) than the control group following the intervention, controlling for pretest levels. These findings could be due to a variety of factors. First, the intervention may have been ineffective at enhancing career adaptability among US college students. Our intervention utilized two 1.5-hour workshops over the course of 2 weeks, while many previous studies used semester-long training classes with weekly assignments to instill learning. It is possible that the shorter duration limited participants’ ability to absorb information and integrate learnings and a longer training with more sessions may have been more beneficial. Van der Horst et al. (2021) attempted to study this relationship by examining intervention intensity (e.g., number of workshops) as a predictor of intervention effectiveness, however, findings were mixed. The most intensive intervention outperformed the two less intensive ones, yet the next best-performing option was the least intensive intervention format. Thus, more research should be conducted to understand the effects of duration on career adaptability intervention effectiveness.

Another potential reason for these findings could be that additional variable(s) were at play. For example, many researchers have suggested that participants’ readiness to benefit from career interventions affects outcomes. Readiness refers to an individual’s ability to make career choices while considering the complexity of family, social, and economic values (Sampson et al., 2000). Sampson et al., (2013) explain that some of the consequences of low readiness in interventions include premature disengagement, negative perception of one’s skills and interests, selective acquisition of incomplete information, and poor evaluation of options among others. They recommend that career interventions include an assessment of readiness to benefit from the intervention. Future research would benefit from considering year in school or career readiness as a screening tool or potentially examining these variables as moderators when conducting an intervention.

Results supported our second hypothesis, indicating that the intervention group had significantly higher levels of career decision self-efficacy following the intervention than the control group. Career decision self-efficacy is one type of self-efficacy that focuses on the process domain (e.g., one’s perception of their ability to navigate the career process) rather than the content domain (e.g., skills for specific career fields) (Hackett & Betz, 1981). The intervention focused on training career adaptability resources and encouraging reflection and reflexivity rather than on specific career paths or action steps, so perhaps it targeted career decision self-efficacy more effectively than other constructs. We chose to measure this type of self-efficacy because of its strong association with career adaptability and its connection to positive career outcomes, but these results may also suggest that it is appropriate to target in brief career interventions. Whiston et al. (2017) found that career decision self-efficacy had the largest effect size (i.e., .446) in career choice interventions, and other brief intervention studies have supported this idea. Berger et al., (2019) used a quasi-experimental design to test a simple feedback mechanism and found that it increased career decision self-efficacy for students with previously misaligned aspirations. The results of the current study show that the career adaptability intervention appeared to enhance career decision-making self-efficacy in a more general population of college students.

Lastly, the results did not support our third hypothesis. The intervention group did not have significantly higher levels of career planning than the control group following the intervention. Again, this may be due to the accelerated time frame of the intervention, readiness to benefit from career interventions or alternative factors. In this study, participants were asked to complete the Time 2 questionnaire within 24 hours following the second intervention session, and it is feasible that this was not long enough for effects to surface. If participants were given more time to practice career planning and reflect on learnings, perhaps results would have differed. Future studies should examine intervention intensity and investigate whether the intervention-to-measurement time period affects outcomes. Additionally, the reliability of the career planning measure in this study was lower than comparable intervention studies, potentially suggesting it is not an effective measure for US college students. It may be beneficial to use alternative career planning measures in future intervention studies or to conduct further measurement research to determine if the scale is generalizable across populations.

Results suggest that a short career adaptability intervention focused on enhancing career adaptability (career concern, career control, career curiosity, career confidence) through psychoeducation and reflection and reflexivity may be effective for increasing career decision self-efficacy among US college students but may not have the power to build individuals’ adapting resources or career planning behaviors.

Limitations and Future Directions

Although this study had many strengths, there were also limitations. First, the attrition rate from Time 1 data collection before the intervention to Time 2 data collection following the intervention was 22% and Schulz and Grimes (2002) recommend that a loss to follow-up of 20% or higher gives concern about the possibility of bias. Although participants in this study were incentivized to complete the study by offering research credits and bonus points and several reminders were sent during data collection, attrition was still apparent. The study’s first wave was conducted at the end of the spring semester close to final exam week, which may have contributed. However, the results of our preliminary analyses indicated there were no significant differences in participants who dropped out of the study compared to those who completed it, so bias is unlikely, but future studies should consider the time of semester and other potential factors that contribute to attrition when conducting intervention studies on college students.

The focus on individual components of career adaptability in this study may also be worth considering. While this is consistent with theory and the state of research, contextual and institutional variables also play a role in the school-to-work transition (Autin et al., 2021; Duffy et al., 2016) and would be valuable to examine in future studies. Another limitation was the use of self-report questionnaires to gather data, which may introduce bias. For example, research suggests that college students can be overconfident in reporting their achievement (Winne & Jamieson-Noel, 2002). To minimize such concerns, participants were reminded that their data would be used for research purposes only and would not impact their educational performance or career prospects. However, future research could use multiple indicators such as professor ratings to measure career adaptability and provide a more holistic representation. Furthermore, the reliability of the career planning measure used in this study was lower than in other comparable studies which may have impacted our results. In our study, omega ω was .64, compared to a Cronbach’s α of .87 reported in Green et al.’s (2020) study among university students in Pakistan, and an internal consistency of .72 which was reported by Zikic and Klehe (2006) for a population of unemployed adults. This may indicate that the career planning measure used was not generalizable to a sample of US students, and future research could identify a more suitable measure. Another potential limitation was the timeframe of data collection. Our goal was to determine whether a shorter intervention would result in the desired effects, but the timeframe from pretest data collection to posttest was only 1 week, and Time 2 data collection was within 24 hours following the intervention, which may limit the ability to determine true effects. Future studies could use a time three data collection to follow up with students after more time has passed. Lastly, a history threat could be possible due to the passage of time between the two waves of the experiment (spring and summer). However, we controlled for this as much as possible by conducting the experiment in back-to-back semesters as well as requiring a 24-hour deadline on questionnaires.

In conclusion, this study found that a short, simplified training intervention focused on career adaptability may not be effective in building career adaptability resources or career planning, but it may have promising potential for enhancing college students’ career decision self-efficacy, an aptitude associated with greater academic and career goals (Choi et al., 2012). This study contributes to the literature by testing a career adaptability intervention on a previously unexamined population (US college students) and demonstrating that not all career adaptability interventions may be effective at building career adaptability. This study suggests that future research should examine which intervention ingredients are required to produce desired results that are generalizable across international samples.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Hannah Wetstone

Author Biographies

Hannah Wetstone is a doctoral student at Georgia State University pursuing her Ph.D. in counseling psychology. She completed her B.S. in human and organizational development at Vanderbilt University and worked as a human capital consultant for a global consulting firm for several years prior to her graduate studies. Today Hannah is a graduate student and research assistant for the Center for the Study of Stress, Trauma, and Resilience. She is interested in studying emerging adults, the school-to-work transition, and the personal or contextual factors that influence work-related or mental health outcomes. In her free time, Hannah enjoys running and traveling.

Kenneth G. Rice, Ph.D., received his Ph.D. in counseling psychology from the University of Notre Dame. He completed post-doctoral training in adolescent mental health at Penn State University. He currently holds the Ken and Mary Matheny Endowed Professorship in the Department of Counseling and Psychological Services at Georgia State University. He also co-directs the Center for the Study of Stress, Trauma, and Resilience, a center devoted to developing multidisciplinary models for understanding and coping with stress that can be applied to real-world settings. He primarily studies the personal characteristics and situational or social factors involved in a wide range of mental health, academic, and work-related outcomes in the USA and with collaborators in other countries. He enjoys travel and reading non-academic novels.

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