Fabian Handl
Simone Kauffeld- Department of Work, Organizational and Social Psychology, Institute of Psychology, Technische Universitaet Braunschweig, Braunschweig, Germany
Introduction: Understanding sustainable careers—characterized by happiness, health, and productivity—require continuous exploration of their driving factors across diverse labor markets. Building on prior integrations of Social Cognitive Career Theory and sustainable career frameworks, our study investigates key predictors within the Australian workforce. Specifically, it focuses on hope for success and future orientation as psychological factors, which were selected for their potential to trigger career-relevant mechanisms. Correspondingly, training as a contextual influence was differentiated into present- and future-oriented formats to align with this focus on temporal perspectives.
Methods: Using longitudinal data collected over 12 years from employed Australian workers (N = 6.033), latent change scores and multilevel modeling were employed to capture dynamic changes and between-person effects.
Results: Results revealed that only future-oriented training was associated with higher subsequent income, whereas all other training effects were non-significant. Psychological predictors demonstrated consistent significance and yielded aligned patterns: both hope for success and future orientation were positively associated with job satisfaction and income but negatively associated with health. This consistency underscores the complex interplay between psychological traits and career outcomes, emphasizing the unique role of health within the sustainable career paradigm.
Discussion: Through identifying differential effects of psychological predictors, our study extends the theoretical understanding of sustainable careers. Consequently, it offers critical insights for employees striving to build sustainable careers and for organizations seeking to match individual well-being with organizational objectives.
1 Introduction
In an ever-evolving world of work, long-term perspectives on career development are gaining increasing importance. As traditional career paths give way to more dynamic, non-linear trajectories (De Vos et al., 2020), the concept of sustainable careers has emerged as a crucial framework for understanding how individuals can achieve lasting professional success without compromising their well-being or health (Van der Heijden et al., 2020).
Concretely, sustainable careers shift the focus away from short-term achievements, such as promotions or salary increases, toward a more holistic and dynamic comprehension of career outcomes over time (De Vos et al., 2020). They are driven by the continuous interaction between the individual, their changing personal and professional context, and the passage of time (De Vos et al., 2020). According to the widely cited model by Van der Heijden and De Vos (2015), sustainable careers can be assessed through three key indicators: productivity as an objective measure, alongside happiness and health as subjective dimensions. Importantly, these elements should be maintained in balance—improvement in one domain should not come at the expense of another (Seibert et al., 2024).
While this broader model provides a valuable framework, its applicability in diverse national contexts remains an open question. Australia offers a particularly insightful case. In addition to rising labor market inequality (Esposto and Agudelo, 2019), it shows specific characteristics compared to other Western countries, such as a less knowledge-based economy (Charles et al., 2021) and a multicultural society with a strong focus on social inclusion (Bouma, 2016).
The wide view of career success in sustainable careers is not only theoretically significant but also practically relevant. For individuals, sustainable careers provide a framework for long-term growth, satisfaction, and resilience. For organizations, they are associated with increased productivity, employee engagement, and competitive advantage (Donald et al., 2024).
This relevance has led to recent efforts to integrate the sustainable career theory with established career models, most notably Social Cognitive Career Theory (SCCT) (Author and Author, n.d.).1 Similar to sustainable careers, SCCT employs various individual and contextual variables to explain career outcomes, proposing cognitive-behavioral processes (Lent et al., 1994). SCCT aims to establish an integrated framework that addresses the limitations of traditional theories, which often treat predictor levels separately. Recent applications of SCCT have increasingly adopted longitudinal perspectives and examined career outcomes beyond early career stages (Akkermans et al., 2024; Damodar et al., 2024), yet only few studies combine longitudinal data with multidimensional indicators such as income, satisfaction, and health.
Meta-analytic evidence from career research confirms that numerous variables influence career success across different levels, validating this framework. Despite the long history of research and the identification of numerous predictors, significant gaps remain, for example the need to transfer and replicate findings in different cultural contexts (Biscoe et al., 2018), ensure causal inference (Spurk et al., 2019), and identify the most fitting theoretical framework (Spurk et al., 2019).
Building on these gaps, SCCT is applied to assess how sustainable career outcomes (job satisfaction, income, and health) are influenced by two predictors levels in the culturally relevant context of Australia. Employing a longitudinal design, which allows for an approximation of causality. The predictor levels reflect a strong future focus, in line with the theoretical foundations. At the contextual level, we examine the impact of training, distinguishing between present-oriented and future-oriented approaches. At the individual level, we examine two psychological characteristics with future focus: hope for success and future orientation. Previous research has demonstrated that these traits positively influence career development mechanisms (e.g. Ginevra et al., 2018; Zaleszczyk and Kot, 2019), suggesting their potential as predictors of sustainable career outcomes. Within the SCCT framework, such future-focused dispositions can be understood as distal antecedents that activate its central mechanisms: individuals who anticipate future opportunities and trust in their capacity to succeed are more likely to believe in their abilities (self-efficacy), expect positive returns from effort (outcome expectations), and pursue ambitious long-term objectives (goals). The research model and its tested hypotheses are presented in Figure 1. To enhance interpretability, the figure also incorporates the final results by indicating positive (+), negative (–), or non-significant (n.s.) associations.
Figure 1. Conceptual research model.
Using this approach, our study makes several significant contributions, especially to the fields of career and training research. In career research, it further advances the integration of Social Cognitive Career Theory (SCCT) and sustainable careers. This was initiated by Author and Author (n.d.) (see text footnote 1) by stressing their shared principles, such as the temporal dynamics of careers and the importance of individual and contextual factors. Such an approach broadens the understanding of career success beyond salary metrics, adopting a longitudinal perspective over a substantial 12-year period. After much empirical work has focused on Western European and North American samples, the study tests findings from the British context (see text footnote 1)—regarding training, psychological characteristics, SCCT, and sustainable careers—in the Australian context. This context represents a different labor market environment with its own multiple structural, cultural, and institutional dimensions (Gregory, 2002; Barrett, 2018). Consequently, the derivation of generalizable knowledge into career development across diverse conditions are possible, enabling a more generalizable comprehension of career sustainability.
Regarding the predictor level for career success, particular attention is given to the temporal, future-oriented component in the selection of predictors, in order to align with the focus of the theories. While the psychological characteristics hope for success and future orientation are distinguished, training is differentiated between present-oriented vs. future-oriented training approaches, providing comparative insights into their respective efficacies. This directly contributes to training research, because it differentiates between the long-term impacts of training on career outcomes and compares the effectiveness of distinct types of training, contributing to a deeper understanding of how training interventions can gradually influence career trajectories.
Practical guidance arises from identifying central individual and contextual factors that shape sustainable careers. By shedding light on these critical predictors, this study equips employees and organizations with the knowledge needed to focus their energy and investments strategically in order to enhance career development outcomes.
2 Theoretical background
2.1 Sustainable career outcomes
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).
2.2 Psychological characteristics
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.
2.2.1 Hope for success
The construct of hope for success can be defined as “the degree to which an individual favors situations in which they are challenged and can test their capabilities.” Hope for success, alongside fear of failure, forms a core part of the wider construct of achievement motivation and is associated with approach-oriented behaviors (Schüler et al., 2013).
Concretely, the literature mentions various traits linked to individuals exhibiting high levels of hope for success, many of which are theoretically conducive to long-term career advancement. These individuals are often characterized by their ability to demonstrate responsibility, a capacity to overcome obstacles as well as perseverance in achieving their goals (Zaleszczyk and Kot, 2019) and adopt active problem-solving strategies to address challenges (Zaleszczyk and Kot, 2019).
Despite its theoretical relevance, the body of research dedicated to hope for success, especially its implications for long-term career outcomes, remains limited. Existing studies, however, show its associations with less detrimental health and financial management behaviors (Hoffmann and Risse, 2020) and wage levels in addition to increased likelihood of job promotions (Risse et al., 2018; Risse, 2020). Further, in specific groups, studies found positive effects on life satisfaction among referees (Minda, 2020), negative relationship with academic procrastination among students (Akmal et al., 2017) and its mediating role between the Big Five personality traits and achievement goal orientation in athletes (Tomczak et al., 2024).
Although hope for success was not measured at every time point in this study, psychological dispositions in adulthood are generally characterized by temporal stability (Alwin, 1994). This supports the assumption that less frequent measurement is sufficient to capture its potential long-term effects. Hope for success has been linked to traits such as perseverance, responsibility, and active problem-solving (Zaleszczyk and Kot, 2019)—all of which are theoretically conducive to sustainable career development. These mechanisms are consistent with key assumptions of SCT, which emphasizes the role of motivational beliefs in shaping long-term vocational outcomes. Based on this theoretical and empirical foundation, we hypothesize that individuals with higher levels of hope for success will demonstrate more favorable outcomes in their career development. The following person-level hypotheses reflect this assumption:
H1: Higher levels of “hope for success” at the given measurement point are associated with elevated overall levels of job satisfaction (H1a), income (H1b), and general health (H1c) across all measurement points.
2.2.2 Future orientation
Future orientation, a construct introduced by developmental psychologists in the 1980s (Johnson et al., 2014), encompasses various definitions. In our study, we define it as the “goal-oriented tendency to focus one's mind on the future and future-valued outcomes” (Praskova and Johnston, 2021). Research has shown that it plays a significant role in long-term career success because it positively influences a range of critical career mechanisms. These include setting clearer goals and demonstrating superior planning skills (Praskova and Johnston, 2021), exhibiting higher career adaptability (Ginevra et al., 2016; Santilli et al., 2017), and making better career choices (Ginevra et al., 2018). Also, the relevance of adaptability and resilience as future-oriented mechanisms for sustainable careers has been demonstrated (Donald et al., 2024).
Despite a significant gap in our understanding of the long-term career impact of future orientation (Praskova and Johnston, 2021), emerging evidence already points to its relevance for sustainable career outcomes. For instance, it has been associated with enhanced work performance (Kooij et al., 2018) and greater job satisfaction (Cernas-Ortiz et al., 2018). Moreover, future orientation has shown links to health-related outcomes in specific populations, such as primary care patients (Hirsch et al., 2015) and older adults (Barnett, 2014), as well as broader indicators like well-being (Felaco and Parola, 2022). These findings highlight its potential and underscore the need for further investigation.
Future orientation was assessed only twice during the study period. However, given that psychological characteristics typically show substantial stability in adulthood (Alwin, 1994), these limited assessments are assumed to sufficiently capture its influence over time. The literature suggests that future-oriented individuals are more likely to engage in career-promoting behaviors such as proactive goal setting, long-term planning, and career adaptability (Ginevra et al., 2018; Praskova and Johnston, 2021). Such behavioral patterns are considered central drivers of sustainable career success. This set of mechanisms aligns with the central tenets of SCCT, which emphasizes the importance of self-regulatory and future-oriented processes in career development. On this basis, we hypothesize that higher levels of future orientation will be associated with more positive career outcomes. The following person-level hypotheses reflect this expectation:
H2: Higher average levels of “future orientation” across the three measurement points are associated with elevated overall levels of job satisfaction (H2a), income (H2b) and general health (H2c) across all measurement points.
2.3 Contextual characteristics
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.
2.3.1 Training
The potential to justify these investments is significantly greater when training has the chance to generate lasting benefits for stakeholders—such as career advancement for individuals (Hansson, 2008) and increased productivity for organizations (Menon, 2013)—provided these outcomes can be clearly demonstrated. Central to this justification is a clear understanding of the conditions under which such investments are most likely to deliver a meaningful return.
Regarding the conditions, within the scope of job-related training, it is common to differentiate between various types of training to account for their diversity. For instance, training can be classified as general (easily transferable) or specific (not easily transferred to another firm) (Luchinskaya and Dickinson, 2019). Building on the time aspect of SCCT and sustainable careers, we distinguish between present-oriented training—which is attended to facilitate the job start, maintain professional status or meet occupational standards—and future oriented-training which prepares for a future job or facilitates promotion. This distinction resonates with emerging work in training research, where scholars have begun to differentiate between short-term, skill-focused programs and developmental training aimed at longer-term career advancement (Mehner et al., 2025).
Despite a substantial body of training research, several critical gaps complicate definitive conclusions. First, many studies have focused primarily on monetary outcomes, such as income effects, while non-monetary benefits like job satisfaction and health remain underexplored (Rueber et al., 2018; Sabates and Hammond, 2008). Second, there is a shortage of longitudinal studies that can assess long-term effects and reduce the risk of overestimating short-term benefits (Offerhaus, 2013; Klink, 2015). Third, there is insufficient knowledge of which types of training are particularly effective, because types are not adequately differentiated (Klink, 2015; Tabvuma et al., 2015; Rueber et al., 2018). These limitations are especially relevant in Australia, where empirical studies remain scarce.
A fundamental challenge in training research is the difficulty of drawing causal conclusions, contributing to the mixed evidence in the field. This is due to the inability to randomly assign participants to training and control groups (Klink, 2015), leading to unobserved heterogeneity: training participants may systematically differ in unobserved characteristics from non-participants, which can bias results, since both the training itself and unobserved factors may drive effects (Offerhaus, 2013; Klink, 2015).
In the following, a summary of the findings related to the three sustainable career outcomes achieved through training is presented. A substantial body of research has explored the relationship between training and wages, often using earnings as a proxy for individual productivity. This is primarily due to the limited availability of direct productivity data (Mazzarolo, 2016). The underlying assumption—derived from neoclassical labor market models—is that in competitive markets, wages reflect marginal productivity (Dearden et al., 2006; Belloni and Villosio, 2015). Several studies have found positive wage effects resulting from training participation. A frequently cited meta-analysis by Haelermans and Borghans (2012) reported an average wage increase of 2.6%, supporting the notion that training enhances productivity. These findings provided early empirical support for training as a worthwhile investment. However, subsequent research has shown that effect sizes tend to decline when studies account for self-selection and use longitudinal data (Haelermans and Borghans, 2012; Siarov, 2012). After the meta-analysis (Haelermans and Borghans, 2012), the empirical picture is still mixed. Some studies report continued positive wage effects (e.g., Almeida and Faria, 2014; Angelopoulos et al., 2017; Blundell et al., 2021). Others suggest limited or no significant effects once confounding variables are included (e.g., Icardi, 2015), also for the Australian context (Coelli and Tabasso, 2019). This contrasts with most international evidence and underscores the need for Australia-specific research. More robust, longitudinal studies are required to clarify whether training outcomes translate into tangible career benefits within the Australian labor market.
The relationship between training and job satisfaction has gained more and more attention. Some investigations have identified a positive correlation (Schmidt, 2004; Budria, 2012), whereas others have found such outcomes confined to specific sub-groups (Georgellis and Lange, 2007; Burgard and Goerlitz, 2014). The evidence concerning specific groups stays inconsistent as well: several studies report favorable effects (Schmidt and Langberg, 2007; Leppel et al., 2012; Visser et al., 2021), including within non-Western settings (Ali et al., 2017; Nauman et al., 2021), yet others highlight either neutral or even adverse results (Hoekstra, 2013; Chime, 2016). In the Australian context, research remains limited: While—to the best of our knowledge—no studies have examined the general population to date, Ayres and Malouff (2007) found positive effects of job training for a specific occupational group, flight attendants. The current state of research underlines the need for further exploration, since the mixed results suggest a complex relationship.
Regarding health, the field of adult learning, encompassing both job-related training and other forms of education like late-life degree attainment (Hatch et al., 2007), highlights a growing recognition of it as an essential non-monetary benefit (Rueber et al., 2018). Studies demonstrates a positive, though modest, relationship between participation in lifelong learning and improved health outcomes (Narushima et al., 2018; Schuller and Desjardins, 2011). These findings are significant because encouraging adults to adopt healthier behaviors is notoriously challenging, given deeply ingrained attitudes (Field, 2011a,b). Beyond behavioral change, health benefits are partly attributed to the economic advantages of lifelong learning. For instance, better job prospects and higher earnings allow individuals to maintain healthier lives (Schuller and Desjardins, 2011). Regardless of the modest impact, these effects underline the broader societal value of promoting adult education.
Although research findings are mixed, positive effects on all three outcomes are assumed. This assumption is based on the core ideas of Social Cognitive Career Theory (SCCT), which emphasizes that learning experiences (which we see as fundamental in training) play a crucial role in shaping future career development (Lent and Brown, 2019).
Despite these positive expectations, the challenge of selection bias in the context of causal inference must be taken into account. This means that exposed participants (in this case, to training) may differ from non-exposed in specific characteristics (Infante-Rivard and Cusson, 2018), which may themselves be associated with higher or lower outcome levels, thereby complicating causal interpretations. To address this, we do not compare training effects between individuals. Instead, we examine whether training triggers positive developments in career success within individuals over the 2 years following their participation, accounting for their overall baseline level of outcomes. Therefore, based on these expectations about within-developments, the evidence from related research, and the central ideas of SCCT, we hypothesize these effects within participants:
H3: Engagement in present-oriented training is associated with increases in job satisfaction (H3a), income (H3b), and general health (H3c) from the year of enrollment over the following two years.
H4: Engagement in future-oriented training is associated with increases in job satisfaction (H4a), income (H4b), and general health (H4c) from the year of enrollment over the following two years.
3 Methods
3.1 Participants and procedures
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.
Table 1. Further sample characteristics.
3.2 Measures
To analyze the proposed hypotheses, the following instruments were used in this study:
3.2.1 Job satisfaction
Job satisfaction was measured annually using six items that assessed different aspects of job satisfaction, including total pay, job security, the nature of the work (what one does), hours worked, flexibility to balance work and non-work commitments, and overall satisfaction. Respondents rated each aspect on a scale from 0 (completely dissatisfied) to 10 (completely satisfied). Cronbach's α for the composite scale ranged from 0.78 (2012) to 0.80 (2018).
3.2.2 Income
Income was annually assessed using a derived variable that captures current weekly gross wages and salaries for all jobs before taxes. However, due to the high variance of this variable, which was too large for various analyses, it was divided by 10.
3.2.3 General health
At each measurement point, general health was assessed using a single question: “In general, would you say your health is…?” Responses were recorded on a 4-point rating scale ranging from 1 (poor) to 5 (excellent).
3.2.4 Hope for success
Hope for success was measured once in 2012 using a subscale of the Achievement Motivation Scale. The Achievement Motivation Scale is based on the Revised Achievement Motives Scale designed and validated by Lang and Fries (2006). Respondents rated their agreement with four items on a 7-point Likert scale, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). A sample item is “I enjoy situations that make use of my abilities.” Cronbach's α for this scale was 0.74.
3.2.5 Future orientation
Future orientation was assessed twice, in 2016 and 2020, using a measure originally developed by Kempson et al. (2013). Respondents were asked to rate their agreement with three items on a 7-point Likert scale, with response options ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). The items of the scale, originally framed as “Present Orientation,” were reversed in accordance with Tahir et al. (2022) to ensure alignment with the future-focused perspective of the study. An example item is “The future will take care of itself.” Cronbach's α was 0.72 in 2016 and 0.71 in 2020.
3.2.6 Training (present-oriented and future-oriented)
Initially, participants had to affirm involvement in educational or training schemes related to their employment over the last 12 months. To make sure a focus on training of moderate length, only sessions lasting between 5 and 50 hours were included, with duration determined by multiplying the number of days by the hours per day. The final measure was based on the number of distinct training spells meeting this duration criterion. Training was categorized into two types based on its objective, which was identified from the response to “What were the aims of any of this training?”. Training aimed at “helping you get started in your job” or “maintaining professional status and/or meeting occupational standards” was classified as present-oriented. Training intended “to prepare you for a future job or to facilitate promotion” was categorized as future-oriented.
3.3 Analytical procedure
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).
4 Results
4.1 Descriptive results
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.
Table 2. Means and standard deviations.
Table 3. Multilevel correlations and ICC (between-person-level and within-person-level).
4.2 Multilevel models
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.
Table 4. Results of the multilevel main effect model.
4.3 Latent change score models
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.
Table 5. Results of the latent change score model (predictors of latent change scores from tx to tx+2).
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.
5 Discussion
Overall, our study explored the complex interplay between psychological traits and contextual influences in shaping sustainable careers within the Australian workforce. Through analyzing hope for success and future orientation as key psychological factors, together with present- and future-oriented training, we sought to understand how these elements collectively contribute to career sustainability over time. We used longitudinal data collected over 12 years. To analyze the data, we applied latent change scores to examine dynamic shifts within participants. In addition, multilevel modeling helped us capture broader patterns of variation across the sample. These methods provided a nuanced view of how individual and contextual variables influence long-term career outcomes in the Australian labor market.
As anticipated, future-oriented training significantly contributes to positive salary development, supporting prior studies that emphasize the importance of training for income progression (e.g., White and Knight, 2018). Although the distinction between training purposes has long been overlooked (Klink, 2015; Tabvuma et al., 2015), recent research (see text footnote 1) has begun to demonstrate its relevance—an idea further reinforced by the present study.
However, contrary to expectations, the positive effects of future-oriented training did not extend to outcomes like job satisfaction and health. Similarly, present-oriented training did not yield significant effects across any of the three outcomes. This may be due to trade-offs: Employees who secure higher-paying positions may face increased responsibility and stress (Johnston and Lee, 2013), which could detract from their satisfaction and health. For present-oriented training, its attention on immediate job performance rather than fostering enduring development likely contributed to its lack of impact.
Consistent with our hypotheses, both psychological constructs (hope for success and future orientation) showed positive effects on job satisfaction and income. These findings expand previous research on the relevance of both constructs (e.g., Ginevra et al., 2018; Zaleszczyk and Kot, 2019) by illustrating how they can predict sustainable, long-term career outcomes.
In contrast, both constructs showed unexpected negative associations with health. While a future-focused outlook and striving for challenging situations are often regarded as resources, they may also create trade-offs: individuals high in these orientations might be more susceptible to sustained effort and increased workload demands. Research on work intensification has shown that rising work demands and responsibilities are consistently associated with stress and deteriorating health outcomes (Mauno et al., 2022). This perspective aligns with Conservation of Resources Theory, which highlights how resource depletion undermines well-being (Hobfoll et al., 2018). Overcommitment may represent a further, though less consistently documented, pathway, as recent studies link it to burnout symptoms and psychosomatic complaints (Thielmann et al., 2024).
5.1 Theoretical implications
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.
5.2 Practical implications
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.
5.3 Limitations and future research
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).
5.4 Conclusion
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.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://melbourneinstitute.unimelb.edu.au/hilda/for-data-users.
Ethics statement
The studies involving humans were approved by Human Research Ethics Committee (The University of Melbourne). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants' legal guardians/next of kin in accordance with the national legislation and institutional requirements.
Author contributions
FH: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. SK: Conceptualization, Funding acquisition, Supervision, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Acknowledgments
The author gratefully acknowledges the efforts of all those involved in the design and collection of the HILDA data. The findings and views reported in this paper, however, are those of the author[s] and should not be attributed to the Australian Government, DSS, or any of DSS' contractors or partners. We acknowledge the support by the Open Access Publication Funds of the Technische Universitaet Braunschweig.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Footnotes
1. ^Author and Author (n.d.). Details Omitted for Double-Blind Review. Manuscript under review.
References
Akkermans, J., da Motta Veiga, S. P., Hirschi, A., and Marciniak, J. (2024). Career transitions across the lifespan: a review and research agenda. J. Vocat. Behav. 148:103957. doi: 10.1016/j.jvb.2023.103957
Akmal, Z. S., Arlinkasari, F., and Febriani, U. A. (2017). Hope of success and fear of failure predicting academic procrastination students who working on a thesis. GUIDENA 7, 78–86. doi: 10.24127/gdn.v7i1.724
Ali, A., Huang, J., Ali, Z., and Li, Z. (2017). “The effects of empowerment, training, and teamwork on employee job satisfaction: case of the agricultural manufacturing sector in Khyber Pakhtunkhwa Province, Pakistan,” in Proceedings of the 2017 International Conference on Education, Economics and Management Research (ICEEMR 2017) (Paris: Atlantis Press) 32–39.
Almeida, R. K., and Faria, M. (2014). The wage returns to on-the-job training: evidence from matched employer-employee data. IZA J. Labor Dev. 3, 1–33. doi: 10.1186/2193-9020-3-19
Alwin, D. F. (1994). “Aging, personality, and social change: the stability of individual differences over the adult life span,” in Life-Span Development and Behavior, Eds. D. L. Featherman, R. M. Lerner, and M. Perlmutter (Hillsdale, NJ: Erlbaum) 135–186.
Angelopoulos, K., Benecchi, A., and Malley, J. (2017). Can Subsidising Job-Related Training Reduce Inequality? CESifo Working Paper No. 6605. Glasgow: Adam Smith Business School, University of Glasgow.
Ayres, J., and Malouff, J. M. (2007). Problem-solving training to help workers increase positive affect, job satisfaction, and life satisfaction. Eur. J. Work Organ. Psychol. 16, 279–294. doi: 10.1080/13594320701391804
Barnett, M. D. (2014). Future orientation and health among older adults: the importance of hope. Educ. Gerontol. 40, 745–755. doi: 10.1080/03601277.2014.898496
Barrett, G. (2018). The labor market in Australia, 2000–2016. IZA World Labor. doi: 10.15185/izawol.443
Belloni, M., and Villosio, C. (2015). Training and wages of older workers in Europe. Eur. J. Ageing 12, 7–16. doi: 10.1007/s10433-014-0327-7
Biscoe, J., Dickmann, M., Hall, T., Parry, E., Mayrhofer, W., and Smale, A. (2018). “Career success in different countries: reflections on the 5C project,” in The Management of Global Careers: Exploring the Rise of International Work, Eds. M. Dickmann, V. Suutari, and O. Wurtz (London: Palgrave Macmillan), 117–148.
Blundell, R., Costa-Dias, M., Goll, D., and Meghir, C. (2021). Wages, experience, and training of women over the life cycle. J. Labor Econ. 39, 275–315. doi: 10.1086/711400
Bouma, G. D. (2016). The role of demographic and socio-cultural factors in Australia's successful multicultural society: how Australia is not Europe. J. Sociol. 52, 759–771. doi: 10.1177/1440783315584210
Budria, S. (2012). The shadow value of employer-provided training. J. Econ. Psychol. 33, 494–514. doi: 10.1016/j.joep.2012.01.001
Burgard, C., and Goerlitz, K. (2014). Continuous training, job satisfaction and gender: an empirical analysis using German panel data. Evid.-Based HRM 2, 126–144. doi: 10.1108/EBHRM-11-2012-0016
Cernas-Ortiz, D. A., Mercado-Salgado, P., and Davis, M. (2018). Future time perspective, job satisfaction, and organizational commitment: the mediating effect of self-efficacy, hope, and vitality. J. Work Organ. Psychol. 34, 1–9. doi: 10.5093/jwop2018a1
Charles, M. B., Ryan, N., Tuffley, D., Noble, D., and Stonecash, R. (2021). Australian attitudes towards innovation, work and technology: towards a cultural explanation. Prometheus 37, 54–68. doi: 10.13169/prometheus.37.1.0054
Chime, E. C. (2016). Investigating the Extent to Which On-the-Job Training Influences Job Satisfaction and Examining Other Contributory Factors in the Irish Retail Sector (Master's thesis). National College of Ireland, Dublin, Ireland.
Coelli, M., and Tabasso, D. (2019). Where are the returns to lifelong learning? Empir. Econ. 57, 205–237. doi: 10.1007/s00181-018-1433-8
Damodar, P., Shetty, A., Dsouza, M. P., Prakash, A., and Gudi, N. (2024). Crafting careers through theory-driven interventions: a scoping review of the utility of social cognitive career theory and career maturity intervention. Int. J. Adolesc. Youth 29:2308081. doi: 10.1080/02673843.2024.2308081
De Vos, A., Van der Heijden, B. I. J. M., and Akkermans, J. (2020). Sustainable careers: towards a conceptual model. J. Vocat. Behav. 117:103196. doi: 10.1016/j.jvb.2018.06.011
Dearden, L., Reed, H., and Van Reenen, J. (2006). The impact of training on productivity and wages: evidence from British panel data. Oxf. Bull. Econ. Stat. 68, 397–421. doi: 10.1111/j.1468-0084.2006.00170.x
Donald, W. E., Van der Heijden, B. I. J. M., and Manville, G. (2024). (Re) framing sustainable careers: toward a conceptual model and future research agenda. Career Dev. Int. 29, 513–526. doi: 10.1108/CDI-02-2024-0073
Dorsett, R., Lui, S., and Weale, M. (2016). The effect of lifelong learning on men's wages. Empir. Econ. 51, 737–762. doi: 10.1007/s00181-015-1024-x
Dramé, M., Cantegrit, E., and Godaert, L. (2023). Self-rated health as a predictor of mortality in older adults: a systematic review. Int. J. Environ. Res. Public Health 20:3813. doi: 10.3390/ijerph20053813
Enders, C. K., and Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Struct. Equ. Model. 8, 430–457. doi: 10.1207/S15328007SEM0803_5
Esposto, A., and Agudelo, J. F. (2019). Casualisation of work and inequality in the Australian labour market. Aust. J. Labour Econ. 22, 53–74. doi: 10.3316/ielapa.248880615722229
Felaco, C., and Parola, A. (2022). Subjective well-being and future orientation of NEETs: evidence from the Italian sample of the European social survey. Soc. Sci. 11:482. doi: 10.3390/socsci11100482
Field, J. (2011b). Researching the benefits of learning: the persuasive power of longitudinal studies. Lond. Rev. Educ. 9, 283–292. doi: 10.1080/14748460.2011.616320
Georgellis, Y., and Lange, T. (2007). Participation in continuous, on-the-job training and the impact on job satisfaction: longitudinal evidence from the German labour market. Int. J. Hum. Resour. Manag. 18, 969–985. doi: 10.1080/09585190701321112
Ginevra, M. C., Annovazzi, C., Santilli, S., Di Maggio, I., and Camussi, E. (2018). Breadth of vocational interests: the role of career adaptability and future orientation. Career Dev. Q. 66, 233–245. doi: 10.1002/cdq.12145
Ginevra, M. C., Pallini, S., Vecchio, G. M., Nota, L., and Soresi, S. (2016). Future orientation and attitudes mediate career adaptability and decidedness. J. Vocat. Behav. 95–96, 102–110. doi: 10.1016/j.jvb.2016.08.003
Gregory, R. G. (2002). “Australian responses to increases in unemployment and wage inequality: comparisons with the United Kingdom, New Zealand and the United States,” in Inequality Around the World, Ed. R. B. Freeman (Basingstoke: Palgrave Macmillan UK) 38–59.
Gucciardi, D. F., Law, K. H., Guerrero, M. D., Quested, E., Thøgersen-Ntoumani, C., Ntoumanis, N., et al. (2020). Longitudinal relations between psychological distress and moderate-to-vigorous physical activity: a latent change score approach. Psychol. Sport Exerc. 47:101490. doi: 10.1016/j.psychsport.2019.02.005
Haelermans, C., and Borghans, L. (2012). Wage effects of on-the-job training: a meta-analysis. Br. J. Ind. Relat. 50, 502–528. doi: 10.1111/j.1467-8543.2012.00890.x
Hansson, B. (2008). Job-Related Training and Benefits for Individuals: A Review of Evidence and Explanations. OECD Education Working Papers No. 19. Paris: OECD Publishing.
Hatch, S. L., Feinstein, L., Link, B. G., Wadsworth, M. E. J., and Richards, M. (2007). The continuing benefits of education: adult education and midlife cognitive ability in the British 1946 birth cohort. J. Gerontol. B Psychol. Sci. Soc. Sci. 62, 404–414. doi: 10.1093/geronb/62.6.S404
Herrmann, A., Hirschi, A., and Baruch, Y. (2015). The protean career orientation as predictor of career outcomes: evaluation of incremental validity and mediation effects. J. Vocat. Behav. 88, 205–214. doi: 10.1016/j.jvb.2015.03.008
Hirsch, J. K., Molnar, D., Chang, E. C., and Sirois, F. M. (2015). Future orientation and health quality of life in primary care: vitality as a mediator. Qual. Life Res. 24, 1653–1659. doi: 10.1007/s11136-014-0901-7
Hobfoll, S. E., Halbesleben, J., Neveu, J.-P., and Westman, M. (2018). Conservation of resources in the organizational context: the reality of resources and their consequences. Annu. Rev. Organ. Psychol. Organ. Behav. 5, 103–128. doi: 10.1146/annurev-orgpsych-032117-104640
Hoekstra, B. (2013). Relating Training to Job Satisfaction: A Survey of Online Faculty Members (Doctoral Thesis). Dordt University, Sioux Center, IA.
Hoffmann, A. O. I., and Risse, L. (2020). Do good things come in pairs? How personality traits help explain individuals' simultaneous pursuit of a healthy lifestyle and financially responsible behavior. J. Consum. Aff. 54, 1082–1120. doi: 10.1111/joca.12317
Icardi, R. (2015). Is workplace training more beneficial for vocationally educated workers? A comparison between England and Germany. J. Context. Econ. Schmollers Jahrb. 135, 67–81. doi: 10.3790/schm.135.1.67
Infante-Rivard, C., and Cusson, A. (2018). Reflection on modern methods: selection bias—a review of recent developments. Int. J. Epidemiol. 47, 1714–1722. doi: 10.1093/ije/dyy138
Jankowsky, K., and Schroeders, U. (2022). Validation and generalizability of machine learning prediction models on attrition in longitudinal studies. Int. J. Behav. Dev. 46, 169–176. doi: 10.1177/01650254221075034
Johnson, S. R. L., Blum, R. W., and Cheng, T. L. (2014). Future orientation: a construct with implications for adolescent health and wellbeing. Int. J. Adolesc. Med. Health 26, 459–468. doi: 10.1515/ijamh-2013-0333
Johnston, D. W., and Lee, W. S. (2013). Extra status and extra stress: are promotions good for us? ILR Rev. 66, 32–54. doi: 10.1177/001979391306600102
Kempson, E., Perotti, V., and Scott, K. (2013). Measuring Financial Capability: A New Instrument and Results From Low- and Middle-income Countries. Washington, DC: World Bank.
Klink, J. (2015). Berufliche Weiterbildung von Hochschulabsolventen (Doctoral Thesis). University of Konstanz, Konstanz, Germany. German.
Klopack, E. T., and Wickrama, K. A. S. (2020). Modeling latent change score analysis and extensions in Mplus: a practical guide for researchers. Struct. Equ. Model. 27, 97–110. doi: 10.1080/10705511.2018.1562929
Kooij, D. T. A. M., Kanfer, R., Betts, M., and Rudolph, C. W. (2018). Future time perspective: a systematic review and meta-analysis. J. Appl. Psychol. 103, 867–893. doi: 10.1037/apl0000306
Lang, J. W. B., and Fries, S. (2006). A revised 10-item version of the Achievement Motives Scale. Eur. J. Psychol. Assess. 22, 216–224. doi: 10.1027/1015-5759.22.3.216
Lent, R. W., and Brown, S. D. (2019). Social cognitive career theory at 25: empirical status of the interest, choice, and performance models. J. Vocat. Behav. 115, 563–578. doi: 10.1016/j.jvb.2019.06.004
Lent, R. W., Brown, S. D., and Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. J. Vocat. Behav. 45, 79–122. doi: 10.1006/jvbe.1994.1027
Leppel, K., Brucker, E., and Cochran, J. (2012). The importance of job training to job satisfaction of older workers. J. Aging Soc. Policy 24, 62–76. doi: 10.1080/08959420.2012.629136
Luchinskaya, D., and Dickinson, P. (2019). ‘Virtuous' and ‘vicious' circles? Adults' participation in different types of training in the UK and its association with wages. Soc. Incl. 7, 177–201. doi: 10.17645/si.v7i3.2039
Mauno, S., Herttalampi, M., Minkkinen, J., Feldt, T., and Kubicek, B. (2022). Is work intensification bad for employees? A review of outcomes for employees over the last two decades. Work Stress 37. doi: 10.1080/02678373.2022.2080778
Mazzarolo, M. (2016). Does Training Reduce Sickness Absenteeism of Older Workers? Why Perceived Job Quality May Matter (Unpublished Master's Thesis). Ca' Foscari University of Venice, Venice, Italy.
McAndrews, L., and Ha-Brookshire, J. E. (2020). Apparel employees' creative, analytical and emotional intelligences and their job and career satisfaction. J. Fashion Mark. Manag. 24, 33–48. doi: 10.1108/JFMM-11-2018-0149
Mehner, L., Rothenbusch, S., and Kauffeld, S. (2025). How to maximize the impact of workplace training: a mixed-method analysis of social support, training transfer and knowledge sharing. Eur. J. Work Organ. Psychol. 34, 201–217. doi: 10.1080/1359432X.2024.2319082
Menon, M. E. (2013). Productivity gains from training: the views of employers and stakeholders. J. Educ. Train. Stud. 1, 118–128. doi: 10.11114/jets.v1i1.54
Meyer, A., and Richter, D. (2024). Longitudinal effects of a professional development program on principals' leadership practices: results of a baseline latent change score model. Prof. Dev. Educ. 1–18. doi: 10.1080/19415257.2024.2413119. [Epub ahead of print].
Minda, M. (2020). Hope for success and life satisfaction among sport referees. J. Educ. Health Sport 10, 720–729. doi: 10.12775/JEHS.2020.10.09.087
Muthén, L. K., and Muthén, B. O. (1998–2017). Mplus User's Guide. 8th Edn. Los Angeles, CA: Muthén & Muthén.
Narushima, M., Liu, J., and Diestelkamp, N. (2018). Lifelong learning in active ageing discourse: its conserving effect on wellbeing, health and vulnerability. Ageing Soc. 38, 651–675. doi: 10.1017/S0144686X16001136
Nauman, S., Bhatti, S., Jalil, F., and Bint E Riaz, M. (2021). How training at work influences employees' job satisfaction: roles of affective commitment and job performance. Int. J. Train. Res. 19, 61–76. doi: 10.1080/14480220.2020.1864444
Ng, T. W. H., Eby, L. T., Sorensen, K. L., and Feldman, D. C. (2005). Predictors of objective and subjective career success: a meta-analysis. Pers. Psychol. 58, 367–408. doi: 10.1111/j.1744-6570.2005.00515.x
Ng, T. W. H., and Feldman, D. C. (2014). Subjective career success: a meta-analytic review. J. Vocat. Behav. 85, 169–179. doi: 10.1016/j.jvb.2014.06.001
Offerhaus, J. K. (2013). Further Training in Germany: Continuous Participation and the Impact of Attitudes and Personality (Doctoral thesis). University of Bremen, Bremen, Germany.
Park, A., Calderwood, L., and Wong, E. (2019). Participant engagement in longitudinal studies: current practice, opportunities and challenges. Soc. Res. Pract. 7, 1–13.
Praskova, A., and Johnston, L. (2021). The role of future orientation and negative career feedback in career agency and career success in Australian adults. J. Career Assess. 29, 463–485. doi: 10.1177/1069072720980174
Redondo, R., Sparrow, P. R., and Hernández-Lechuga, G. (2021). The effect of protean careers on talent retention: examining the relationship between protean career orientation, organizational commitment, job satisfaction and intention to quit for talented workers. Int. J. Hum. Resour. Manag. 32, 2046–2069. doi: 10.1080/09585192.2019.1579247
Risse, L., Farrell, L., and Fry, T. R. (2018). Personality and pay: do gender gaps in confidence explain gender gaps in wages? Oxf. Econ. Pap. 70, 919–949. doi: 10.1093/oep/gpy021
Risse, L. (2020). Leaning in: is higher confidence the key to women's career advancement? Aust. J. Labour Econ. 23, 43–78.
Rueber, I. E., Rees, S. L., and Schmidt-Hertha, B. (2018). Lifelong learning–lifelong returns? A new theoretical framework for the analysis of civic returns on adult learning. Int. Rev. Educ. 64, 543–562. doi: 10.1007/s11159-018-9738-6
Rush, J., Rast, P., Almeida, D. M., and Hofer, S. M. (2019). Modeling long-term changes in daily within-person associations: an application of multilevel SEM. Psychol. Aging 34, 163–176. doi: 10.1037/pag0000331
Sabates, R., and Hammond, C. (2008). The Impact of Lifelong Learning on Happiness and Well-being. London: NIACE.
Sadia, R. (2016). The relationship between employee health, quality culture and organizational effectiveness: findings from the literature. Int. J. Des. Nat. Ecodyn. 11, 1–9. doi: 10.2495/DNE-V11-N1-1-9
Santilli, S., Marcionetti, J., Rochat, S., Rossier, J., and Nota, L. (2017). Career adaptability, hope, optimism, and life satisfaction in Italian and Swiss adolescents. J. Career Dev. 44, 62–76. doi: 10.1177/0894845316633793
Schafer, J. L., and Graham, J. W. (2002). Missing data: our view of the state of the art. Psychol. Methods 7, 147–177. doi: 10.1037/1082-989X.7.2.147
Schmidt, E. K., and Langberg, K. (2007). Academic autonomy in a rapidly changing higher education framework: academia on the Procrustean bed? Eur. Educ. 39, 80–94. doi: 10.2753/EUE1056-4934390406
Schmidt, S. W. (2004). “The relationship between satisfaction with on-the-job training and overall job satisfaction,” in Proceedings of the 2004 Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education, Ed. M. Glowacki-Dudka (Muncie, IN: Ball State University) 188–193.
Schüler, J., Brandstätter, V., and Baumann, N. (2013). Failure cue priming and impaired cognitive performance—analyses of avoidance motivation as a mediator and fear of failure as a moderator. Eur. J. Soc. Psychol. 43, 335–343. doi: 10.1002/ejsp.1942
Schuller, T., and Desjardins, R. (2011). “Wider benefits of adult education,” in Adult Learning and Education, Ed. K. Rubenson (Amsterdam: Academic Press) 294–298.
Seibert, S. E., Akkermans, J., and Liu, C. H. (2024). Understanding contemporary career success: a critical review. Annu. Rev. Organ. Psychol. Organ. Behav. 11, 509–534. doi: 10.1146/annurev-orgpsych-120920-051543
Siarov, L. (2012). The Economics of Lifelong Learning and Skill Formation: Risk, Return and Individual Choice (Doctoral thesis). Jacobs University Bremen, Bremen.
Spurk, D., Hirschi, A., and Dries, N. (2019). Antecedents and outcomes of objective versus subjective career success: competing perspectives and future directions. J. Manag. 45, 35–69. doi: 10.1177/0149206318786563
Tabvuma, V., Georgellis, Y., and Lange, T. (2015). Orientation training and job satisfaction: a sector and gender analysis. Hum. Resour. Manag. 54, 303–321. doi: 10.1002/hrm.21650
Tahir, M. S., Shahid, A. U., and Richards, D. W. (2022). The role of impulsivity and financial satisfaction in a moderated mediation model of consumer financial resilience and life satisfaction. Int. J. Bank Mark. 40, 773–790. doi: 10.1108/IJBM-09-2021-0407
Thielmann, B., Pohl, R., and Boeckelmann, I. (2024). Overcommitment, work-related behavior, and cognitive and emotional irritation in veterinarians: a comparison of different veterinary working fields. Healthc. 12:1514. doi: 10.3390/healthcare12151514
Tomczak, M., Kleka, P., Tomczak-Łukaszewska, E., Walczak, M., and Steinborn, M. B. (2024). Hope for success as a mediator between big five personality traits and achievement goal orientation among high performance and recreational athletes. PLoS ONE 19:e0288859. doi: 10.1371/journal.pone.0288859
Turban, D. B., Moake, T. R., Wu, S. Y. H., and Cheung, Y. H. (2017). Linking extroversion and proactive personality to career success: the role of mentoring received and knowledge. J. Career Dev. 44, 20–33. doi: 10.1177/0894845316633788
Van der Heijden, B., De Vos, A., Akkermans, J., Spurk, D., Semeijn, J., Van der Velde, M., et al. (2020). Sustainable careers across the lifespan: moving the field forward. J. Vocat. Behav. 117:103344. doi: 10.1016/j.jvb.2019.103344
Van der Heijden, B. I. J. M., and De Vos, A. (2015). “Sustainable careers: introductory chapter,” in Handbook of Research on Sustainable Careers, Eds. A. De Vos and B. I. J. M. Van der Heijden (Cheltenham: Edward Elgar), 1–19.
Visser, M., Loessbroek, J., and Van der Lippe, T. (2021). The use of HR policies and job satisfaction of older workers. Work Aging Retire. 7, 303–321. doi: 10.1093/workar/waaa023
Wang, D., Liu, X., and Deng, H. (2022). The perspectives of social cognitive career theory approach in current times. Front. Psychol. 13:1023994. doi: 10.3389/fpsyg.2022.1023994
Watson, N. (2021). Finding your way around the HILDA survey data. Aust. Econ. Rev. 54, 554–564. doi: 10.1111/1467-8462.12437
White, M., and Knight, G. (2018). Training, job mobility and employee satisfaction. J. Educ. Work 31, 447–460. doi: 10.1080/13639080.2018.1513639
Yang, F., and Chau, R. (2016). Proactive personality and career success. J. Manag. Psychol. 31, 467–482. doi: 10.1108/JMP-04-2014-0139
Keywords: sustainable careers, SCCT, Australian workforce, cross-cultural replication, future focus
Citation: Handl F and Kauffeld S (2025) Psychological characteristics and training influences on sustainable careers: a 12-year Australian perspective. Front. Organ. Psychol. 3:1664677. doi: 10.3389/forgp.2025.1664677
Received: 12 July 2025; Accepted: 24 October 2025;
Published: 20 November 2025.
Edited by:
Bianca Ifeoma Chigbu, Walter Sisulu University, South AfricaReviewed by:
Afnan Rahman, HBS Medical and Dental College, PakistanAzizi Ab Malek, Ministry of Health, Malaysia
Edil Wijaya Nur, SMAN 2 Makassar, Indonesia
Yuniadi Mayowan, Brawijaya University Hospital, Indonesia
Copyright © 2025 Handl and Kauffeld. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Fabian Handl, ZmFiaWFuLmhhbmRsQG1haWxib3gub3Jn