Before presenting the specific research in detail, this paper first introduces the key outcomes of sustainable careers that will be examined. In recent years, this topic has gained significant traction in career success research, as evidenced by two comprehensive reviews (De Vos et al., 2020; Donald et al., 2024). Given the variety of definitions in the literature, this paper follows the sustainable career framework proposed by De Vos et al. (2020) and focuses on three key outcomes: Happiness, health, and productivity.

As an objective indicator, productivity refers to strong performance in one's current job as well as high employability or career potential. In contrast, two rather subjective concepts are also considered. Happiness captures the subjective elements of feeling successful or satisfied with one's career, yet seen from a wider life perspective. Health reflects the dynamic fit of the career with one's mental and physical capacities, encompassing both physical and mental health. To fully understand these outcomes, it is essential to examine them longitudinally and in parallel.

Beyond short-term dynamics, a long-term perspective is crucial for career success research (Seibert et al., 2024). The impact of career-related experiences or events may only become apparent after a significant delay, highlighting the time-sensitive nature of this concept (De Vos et al., 2020).

Personality, alongside human capital and demographics, has emerged as an important factor influencing career success. This is underlined by two meta-analyses (Ng et al., 2005; Ng and Feldman, 2014) that show that specific psychological characteristics like core self-evaluation and locus of control have a greater impact than broader personality measures like the Big Five. In recent years, more studies have underscored the importance of additional traits, including proactivity (e.g., Yang and Chau, 2016; Turban et al., 2017), emotional intelligence (e.g., McAndrews and Ha-Brookshire, 2020), and protean career attitudes (e.g., Herrmann et al., 2015; Redondo et al., 2021). Despite this promising progress, significant gaps persist, stressing the need for further investigation into the long-term relevance of other specific traits for career success.

Next to individual characteristics, numerous studies have identified contextual characteristics as key predictors of career success. These are often framed in more specific subcategories in meta-analyses and reviews, e.g. organizational sponsorship (Ng et al., 2005) and internal and external labor markets as well as sponsorship, developmental support, and developmental networks (Seibert et al., 2024). The importance of those categories is emphasized by their prominent role in the respective articles, each contributing significantly to the explanation of career success (Ng et al., 2005; Ng and Feldman, 2014). Within this framework, we classify externally offered training as part of the subcategory of organizational sponsorship (Ng et al., 2005) and sponsorship, developmental support, and developmental networks (Seibert et al., 2024). This reflects the organization's investment in the employee's development and thus serves as a contextual characteristic relevant to career success.

To empirically assess these hypothesized relationships, this study builds on a prior analysis (see text footnote ¹) conducted using data from the UK Household Longitudinal Study (University of Essex). For this purpose, we draw on data from the Household, Income and Labour Dynamics in Australia (HILDA) survey, a nationally representative, household-based longitudinal study that began in 2001. Each year, HILDA surveys over 7,000 households and more than 17,000 individuals aged 15 and older, collecting detailed information through self-completion questionnaires and face-to-face interviews. The survey captures a wide range of variables (around 1,400), providing comprehensive insights into household and individual circumstances across Australia. For an in-depth look at the survey's design, refer to Watson (2021) and for access to user guides, technical documents, and related literature, visit https://melbourneinstitute.unimelb.edu.au/hilda. Specifically, we use data from wave 11 (2011) to wave 22 (2022).

Eligibility for the study sample was defined by several criteria. First, participants had to be actively employed in dependent employment, as the study focuses on organizational career contexts where employer-provided training and workplace structures play a central role. Therefore, self-employed individuals were excluded. Second, participants had to be aged 25 or older, as this is the age when most individuals have completed their first full-time education and gained initial work experience (Dorsett et al., 2016). Third, inclusion required attendance at a minimum of 10 out of the 12 measurement points, which ensures a stable observation of individual trajectories while still allowing flexibility for occasional nonresponse. Applying these criteria resulted in a total analytical sample of 6.033 individuals. Due to missing values in certain variables, effective sample sizes vary between analyses; precise numbers are reported in the respective tables.

Baseline characteristics refer to measurements from the first included wave (2011). The average age of participants was 43.91 years (SD = 11.51), with an average of 22.12 years of employment experience (SD = 11.72). Tenure with the current employer averaged 7.60 years (SD = 8.18). Participants worked 37.07 h per week on average (SD = 13.86), earning a mean gross weekly wage of AUD 1,202.99 (SD = 833.81). Furthermore, 17.9% of the sample held multiple jobs, and 28.1% were employed part-time (defined here as ≤ 34 h per week). The gender distribution was balanced, with 51.2% female participants. Additional characteristics regarding qualifications and workplace size are detailed in Table 1.

To analyze the proposed hypotheses, the following instruments were used in this study:

Building on our previous analysis (see text footnote ¹), we applied two advanced statistical approaches using Mplus 8.4 to address our research questions. Specifically, we used multilevel modeling (MLM) to test the effects of individual psychological characteristics (H1 and H2), and latent change score modeling (LCSM) to assess the longitudinal impact of training (H3 and H4). This approach replicates the general modeling logic of our earlier work but applies it to a different set of predictors and outcome dynamics.

Prior to model estimation, we conducted diagnostic checks to ensure the data fulfilled key assumptions for longitudinal modeling. Specifically, we calculated intraclass correlation coefficients (ICC1) for each outcome variable to confirm sufficient within-person variability, thereby justifying the use of MLM and change modeling over time.

To investigate whether stable individual traits—hope for success and future orientation—were systematically linked to long-term levels of job satisfaction, health, and income (H1 and H2), we employed multilevel modeling (MLM) with time-invariant covariates. Model structure followed principles described in Muthén and Muthén (1998–2017, pp. 315), and estimates reflect between-person variation across individuals. By modeling between-person variance, we could assess whether individuals with higher levels of these psychological traits consistently reported more favorable outcomes.

For assessing the influence of training on individual development over time, we used latent change score modeling (LCSM), which is a structural equation modeling (SEM) technique (Gucciardi et al., 2020; Klopack and Wickrama, 2020). In line with our earlier modeling strategy (see text footnote ¹), we implemented univariate models in which training participation at a given wave served as the predictor for subsequent changes in outcome variables over waves. Following prior methodological recommendations (Gucciardi et al., 2020; Klopack and Wickrama, 2020), we incorporated autoregressive paths to account for prior levels of the dependent variable and constrained coefficients across time points to extract generalizable training effects. To better capture developmental trajectories, our models focused on change between t and t+2, which balances avoiding transient short-term effects while not extending so far that effects become obscured by other influences. Because income was divided by 10 to reduce variance, coefficients can be interpreted as effects on weekly income in tens of AUD (i.e., multiplying by 10 yields the effect in raw AUD).

All models used full information maximum likelihood estimation (FIML), which leverages all available data and has been shown to outperform other methods in terms of bias reduction, accuracy and statistical power (Enders and Bandalos, 2001; Schafer and Graham, 2002).

Table 2 displays the means and standard deviations for the variables examined in this study. Further, Table 3 summarizes the intraclass correlations (ICCs) and multilevel correlations (both the within-person and between-person correlations) across all study measures. The ICC(1) values −0.50 for job satisfaction, 0.66 for income, and 0.63 for health—indicate a moderate level of temporal stability. At the same time, they suggest sufficient within-person variability, which justifies the use of multilevel modeling and latent change score approaches in subsequent analyses.

Building on these descriptive insights, we next applied multilevel models to explore the between-person effects of psychological characteristics. In our initial model centered on hope for success, the results largely met our expectations: Participants who exhibited higher levels of hope for success tended to report greater satisfaction (β = 0.173, p < 0.001) and higher income levels over time (β = 11.648, p < 0.001), supporting hypotheses H1a and H1b. Contrary to hypothesis H1c, however, we found a significant association between hope for success and lower overall health levels (β = −0.120, p < 0.001).

In the subsequent model focusing on future orientation, the results aligned with the patterns observed in the hope for success model: Higher levels of future orientation were associated with increased job satisfaction (β = 0.035, p < 0.001) and income (β = 10.848, p < 0.01) over time, supporting hypotheses H2a and H2b. Nevertheless, similar to the findings for hope for success, higher future orientation was linked to lower overall health levels (β = −0.075, p < 0.001), running counter to hypothesis H2c. A detailed overview of these multilevel modeling results is provided in Table 4.

First, we examined the models for present-oriented training, as shown in Table 5. The level of present-oriented training at one wave—constrained equal across waves—does not significantly predict subsequent latent changes in job satisfaction (β = −0.003, SE = 0.007, p = 0.601), income (β = 0.079, SE = 0.535, p = 0.882), or health (β = −0.003, SE = 0.004, p = 0.421) between this wave and two waves later. These findings do not support hypotheses H3a, H3b, or H3c.

Next, we analyzed the models for future-oriented training, also shown in Table 5. The level of future-oriented training at one wave—constrained equal across waves—significantly predicts a subsequent increase in income between this wave and two waves later (β = 5.532, SE = 0.968, p < 0.001). In original units, this equals about 55 AUD additional weekly income growth over the two-year interval. However, no significant effects were found for job satisfaction (β = −0.013, SE = 0.013, p = 0.312) or health (β = 0.005, SE = 0.008, p = 0.527). These results support H4b, but not H4a or H4c. For ease of interpretation, Figure 1 summarizes the final pattern of significant and non-significant effects, using +/–/n.s. signs to indicate the direction of results.

These results yield several theoretical implications, which we discuss in the following. First, the understanding of sustainable careers through emphasizing their multidimensional nature (Donald et al., 2024) is enriched. Findings reveal that individual predictors show differential associations with career outcomes: Some predictors are linked to improvements in one dimension (e.g., happiness) while relating to declines in another (e.g., health). Especially noteworthy is the explicit emphasis on health as an essential component of career sustainability, particularly in comparison to previous research, this study is able to illuminate the specific role of health more clearly. Results demonstrate its unique position, often showing effects distinct from or even contrary to those related to happiness and productivity. Therefore, the importance of treating health as a central part of sustainable career theory, which is provided in the framework (Donald et al., 2024), is underscored. Further, the necessity of identifying potential trade-offs among career dimensions is stressed, advocating for a more comprehensive approach to career interventions that balances potentially competing outcomes.

Moreover, our study enhances the SCCT framework by extending its cultural and temporal applicability, offering valuable new insights. On the one hand, the application of SCCT within the Australian labor market underlines its cross-cultural relevance. It offers empirical validation in a not frequently analyzed context in career research with specific characteristics like less knowledge-based work (Charles et al., 2021) and a taste for hyper-masculine work compared to other Western countries (Charles et al., 2021). On the other hand, the comprehension of SCCT's capacity to account for long-term career effects is strengthened by the longitudinal design. This design is analyzed using latent change scores (Klopack and Wickrama, 2020; see text footnote ¹), which, with change intervals of 2 years, allow for a long-term perspective. In doing so, it is illustrated how predictors drive changes in career outcomes over such time spans, underlining the significance of temporal dynamics within the SCCT framework (Wang et al., 2022).

In addition to the separate contributions to the two career theories, their computability for integration—already initiated within one cultural context (see text footnote ¹) and representing a theoretical advancement in career research—was further demonstrated. In another cultural context, the compatibility between the two approaches was shown through the dimensions of sustainable careers—happiness, health, and productivity (De Vos et al., 2020; Donald et al., 2024)—which can be effectively predicted using SCCT. This highlights not only the measurability of these dimensions within the SCCT framework but also their principal relevance across cultures.

Lastly, this study sheds light on the role of psychological characteristics in shaping sustainable careers. Psychological traits that contain future-oriented components, which emphasize individual agency and are therefore also highlighted in the SCCT (Lent and Brown, 2019), were shown to have significant impacts on career outcomes. Both hope for success and future orientation were positively associated with happiness and income, and negatively associated with health, echoing the trade-offs observed among sustainable career dimensions. The following findings not only underline the nuanced and sometimes paradoxical effects of psychological traits but also reaffirm that the SCCT considers individual characteristics as a central component in understanding career processes.

In sum, our study extends the theoretical frontiers of SCCT and sustainable career frameworks by illustrating their integration, emphasizing health within sustainable careers, expanding SCCT's temporal and cultural applicability, and underscoring the complex role of psychological traits.

In addition to these theoretical insights, our findings also yield actionable implications for individuals and organizations seeking to promote sustainable career development. For employees, future-oriented training has the potential to boost income. However, it does not appear to increase other career outcomes such as satisfaction or health, which underlines the need for employees to carefully choose training to match their career goals. For organizations, investing in future-oriented training (compared to present-oriented training) for their workforce is recommended, since such initiatives may correspond to higher employee income over time. Nonetheless, training should not be viewed as a one-size-fits-all solution. Organizations must carefully select training that aligns with employees' needs and the company's strategic goals to avoid inefficient use of resources.

Next to training, psychological characteristics also take a central function in career development. For individuals, fostering a future-oriented mindset, coupled with optimism, is associated with higher career satisfaction and income. This involves reflecting on personal values and long-term goals and managing possible trade-offs, like health challenges, that might arise from this perspective. Individuals are encouraged to take proactive steps in balancing career aspirations with self-care to mitigate negative effects on well-being. Organizations, meanwhile should actively support a future-focused perspective among their workforce, for example through leadership practices that emphasize hope for success and future orientation. Those principles could be embedded into different formats, e.g., daily management strategies, coaching sessions, and training. However, organizations must also keep attentive to potential negative health outcomes associated with the mentioned strategies, ensuring that health and well-being are integral to career development strategies.

Lastly, the framework of sustainable careers emphasizes that career development often involves goal conflicts—such as between income and health. Improving one outcome may coincide with unintended negative effects in another. Individuals must therefore consider which dimensions align with their long-term values, as gains in one area do not automatically enhance others. If health problems arise despite success in other domains, this may also negatively impact the organization (Sadia, 2016). Recognizing and addressing such tensions is essential to ensure that career progression is not only successful but also sustainable for both individuals and employers.

In summary, career development strategies that emphasize targeted training, psychological growth, and sustainability require a nuanced approach. Individuals and organizations must work collaboratively to identify priorities, address trade-offs, and foster continual well-being.

Several limitations of this research must be acknowledged. First, the non-experimental design of our dataset inherently restricts the capacity to infer strong causal claims. Although the use of latent change score (LCS) modeling helps approximate causality by accounting for baseline variations (Meyer and Richter, 2024), the inability to randomize key predictors, particularly psychological traits, remains a significant limitation. In the context of training interventions, randomization is theoretically more feasible, since researchers could assign participants to either a training group or a control group to evaluate the effects. However, adopting such an approach introduces additional complexities: individuals would no longer decide to participate voluntarily, which could significantly affect their engagement and outcomes. Furthermore, ethical considerations make it impractical to require adults to attend training against their will. These constraints highlight the challenges of achieving true experimental conditions in real-world settings, especially when investigating the subtle impacts of training on career outcomes.

Second, the generalizability of our results is constrained by the study's focus on the Australian labor market. Although Australia represents a typical Western society showing parallels to other labor markets (Gregory, 2002), its unique features and cultural context may limit the applicability of our results to other regions. Future research should replicate these findings in diverse cultural and economic settings to enhance the external validity of the conclusions. Such cross-contextual comparisons would strengthen the theoretical understanding of sustainable careers and confirm the robustness of observed patterns.

Third, the measurement of psychological characteristics and health outcomes involved certain constraints. Psychological traits were assessed infrequently (hope for success once in 2012; future orientation twice in 2016 and 2020). Although adult dispositions are generally regarded as relatively stable over time (Alwin, 1994), this measurement frequency limits the ability to capture within-person changes or the potential impact of major life events (e.g., job changes, personal crises). Future studies with more frequent assessments could better address such dynamics using advanced multilevel techniques (Rush et al., 2019). In addition, general health was measured with a single self-report item. While single-item indicators of global health have demonstrated predictive power in large-scale studies (Dramé et al., 2023), they inevitably provide less nuance than multidimensional health measures. Taken together, these measurement choices strengthen feasibility and comparability but reduce the granularity of inferences.

Finally, panel attrition posed challenges across the 12-year period, since some participants dropped out of the study. Although modern missing data techniques mitigated potential biases, systematic attrition could still influence the results (Jankowsky and Schroeders, 2022), like participants with lower career success being less likely to continue. Future research could explore strategies to minimize attrition, for example offering incentives or personalized mailings and materials, to maintain a representative sample over extended periods (Park et al., 2019).

Our study highlights the multifaceted nature of sustainable careers by demonstrating the distinct effects of psychological traits and contextual factors. Both hope for success and future orientation emerged as critical predictors, influencing all career dimensions in the same direction. Specifically, both were positively associated with income and job satisfaction and negatively associated with health. Similarly, the analysis revealed that only future-oriented training was associated with higher subsequent income, whereas other training analyses showed no effect. Our findings underscore the importance of aligning career strategies to individual and organizational priorities, balancing the pursuit of financial and satisfaction-related goals while safeguarding health.