The concept of career success has evolved significantly over time, moving from a focus on objective indicators to a more nuanced understanding that includes subjective experiences. Traditionally, career success was assessed using measurable and externally visible outcomes such as salary, promotions, and job titles (Ng et al., 2005; Hall, 2012). These objective indicators were widely used due to their comparability across individuals and organizations and their relevance in traditional, hierarchical career structures. However, as workplace dynamics have shifted, driven by flatter organizational hierarchies, individual career ownership, and changing employee values, scholars have increasingly recognized the importance of subjective career success (SCS). Subjective career success refers to an individual’s personal evaluation of his/her career progress, based on criteria such as job satisfaction, perceived achievement, and alignment with personal goals (Poole et al., 1993). This shift acknowledges that individuals may feel highly successful even without formal advancement or financial gains, particularly when their work feels meaningful or personally fulfilling.

To account this complexity, career success is now often studied using multidimensional and subjective approaches. One notable development in this area is the Subjective Career Success Inventory (SCSI) introduced by Shockley et al. (2016). Developed through a mixed-methods process, starting with qualitative interviews and followed by large-scale quantitative testing, the SCSI provides a comprehensive tool for assessing career success based on what individuals value in their careers. The scale has been validated across various contexts and showed strong associations with important career outcomes, such as job satisfaction and job performance, reinforcing the relevance of subjective measures in modern career research.

Building on this evolving understanding, the current study adopted a multidimensional framework for measuring subjective career success, drawing from the foundational work of Gattiker and Larwood (1988). Their model includes five dimensions: (1) Interpersonal success (the quality of relationships and social integration at work), (2) Financial success (satisfaction with income and financial progression), (3) Job success (perceived effectiveness and fulfillment in one’s job role), (4) Hierarchical success (advancement in organizational rank or status), and (5) Life success (overall satisfaction with how one’s career contributes to personal well-being). The first four dimensions, interpersonal, financial, job, and hierarchical, represent organizational career success, reflecting achievements within the workplace. In contrast, life success captures non-organizational outcomes, emphasizing the broader impact of career achievements on personal life satisfaction.

Beyond these conventional outcomes, recent work highlights the value of well-being and emotional resilience as complementary facets of career development. For instance, Lo and Punzalan (2025) showed that positive psychology strategies, such as cultivating a growth mindset, resilience, mindfulness, and gratitude, enhance both emotional well-being and professional satisfaction among higher education instructors. These suggest that lifelong learning initiatives must consider not only career advancement but also holistic well-being.

Existing literature identifies three major perspectives on career commitment, namely the proactive approach, individual differences, and job identification. These perspectives offer a comprehensive foundation for examining how re-engagement with education and academic motivation contribute to career success.

Firstly, several studies have defined career commitment through a proactive approach, emphasizing the individual’s active pursuit of learning, growth, and goal setting (Porath et al., 2012; Maurer and Chapman, 2013; Zhao et al., 2022). In this view, employees proactively seek out development opportunities and take initiative to shape their career paths. This behavior aligns with the concept of a proactive personality of Crant (2000), who explained that individuals who “identify opportunities and act on them, show initiative, take action, and persevere until meaningful change occurs.” Maurer and Chapman (2013) conceptualized career commitment as a personal characteristic inclined toward developmental behaviors, while Zhao et al. (2022) emphasized that proactive employees enhance their employability through adaptability to change. Within the context of lifelong e-learning programs, re-engagement with education reflects this proactive drive, demonstrating a self-initiated investment in professional advancement.

Secondly, another body of research focused on individual differences, suggesting that career commitment is shaped by employees’ unique values, norms, abilities, and attitudes (Newman et al., 2011; Turban et al., 2017; Kaspi-Baruch et al., 2024). In this regard, career commitment is seen as a self-directed process, rooted in personal convictions rather than organizational mandates. Newman et al. (2011) defined career commitment based on an individual’s prior attitudes and values, while Kaspi-Baruch et al. (2024) argued that employees’ own values, rather than those imposed by the organization, primarily guide their commitment. Notably, even employees within the same organization may differ significantly in the degree and nature of their career commitment. In the e-lifelong learning context, this perspective emphasizes the importance of personal motivation and individualized learning paths, as workers seek educational experiences that align closely with their intrinsic goals.

Thirdly, Wolf et al. (1995) and Hsu et al. (2015) defined career commitment through job identification. This approach focuses on the extent to which individuals define themselves through their careers and maintain a strong professional identity. McArdle et al. (2007) explained that career commitment in this sense reflects how individuals perceive themselves within their occupational context. Liu et al. (2023) highlighted that identification with a specific career path reflects an individual’s intention to remain in that occupation over time. In the context of e-lifelong learning, re-engagement with educational programs may serve as a mechanism to reinforce professional identity and ensure continued relevance in an increasingly dynamic labor market.

In the context of this study, these theoretical perspectives explain how re-engagement with education and academic motivation influence workers’ career success. Workers with a proactive orientation are more likely to re-engage in e-learning programs, driven by an intrinsic desire for growth. Those guided by individualized values and self-directed attitudes will select educational paths that align with their personal career aspirations (Pang et al., 2025). Finally, individuals who strongly identify with their professional roles will pursue lifelong learning as a strategy to maintain expertise and professional relevance. Academic motivation, encompassing both intrinsic and extrinsic elements, acts as a catalyst that connects career commitment with learning engagement, thereby shaping career outcomes. Together, these perspectives form a comprehensive framework for understanding the dynamic interplay between re-engagement, motivation, and career success in the era of e-lifelong learning.

Recent theoretical perspectives emphasize that motivation for lifelong learning is deeply intertwined with metacognitive regulation and socioemotional competencies, two core dimensions highlighted in the OECD Learning Compass 2030 and the CASEL Social and Emotional Learning (SEL) Framework (Shek et al., 2025). Metacognition refers to the ability of learners to plan, monitor, and evaluate their own learning processes (Schraw and Moshman, 1995). In the context of e-lifelong learning, these processes enable individuals to set meaningful learning goals, track their progress, and adjust strategies in response to feedback. This reflective regulation is strongly connected to self-determined motivation (Ryan and Deci, 2017), which nurtures autonomy and persistence through self-directed behavior rather than external control.

Emerging research further demonstrates that learning-to-learn, a central metacognitive capability, plays a critical role in sustaining lifelong learning and adaptive performance. For example, Sugiyama et al. (2024) showed that metacognitive awareness and learning-to-learn competencies predict functional outcomes even in rehabilitation contexts, underscoring their generalizability across domains. Similarly, Enachescu (2025) found that microlearning environments enhance learners’ self-reflection and metacognitive regulation, thereby strengthening lifelong learning readiness in higher education. Shi (2024) also highlighted how metacognitive teaching frameworks, when applied through collaborative lesson study, can cultivate planning, monitoring, and reflective evaluation, key processes that mirror self-regulated motivation in adult learning. Collectively, these studies reinforce that motivation and metacognition are mutually reinforcing dimensions of sustained learning engagement.

At the same time, socioemotional competencies, including emotional self-regulation, empathy, resilience, and collaboration, are equally vital in maintaining engagement and psychological well-being. According to the CASEL SEL Framework, learners who can manage their emotions and build positive relationships show greater perseverance, openness to feedback, and adaptability, qualities that directly enhance both learning outcomes and career success. Within adult-learning settings, these socioemotional strengths help learners navigate uncertainty, cope with professional challenges, and collaborate effectively in diverse teams.

Bringing these perspectives together provides a comprehensive understanding of how motivation operates as both a metacognitive and socioemotional mechanism. When individuals internalize learning goals through autonomous motivation, they not only engage in metacognitive behaviors, planning, monitoring, and evaluating, but also draw on socioemotional resources such as self-control, resilience, and empathy to sustain progress. This integration aligns with the OECD Learning Compass 2030, which conceptualizes agency, co-agency, and transformative competence as outcomes of dynamic interactions between cognition, emotion, and motivation.

Based on the objective of this study and the review of existing literature, two main hypotheses are developed to examine the relationships between lifelong learning engagement, academic motivation, and career success.

In this study, we examined various measures of career success and factors motivating academic engagement, as presented in Table 1.

Subjective career success was measured using the multidimensional scale developed by Gattiker and Larwood (1988), which includes five dimensions: job success, interpersonal success, financial success, hierarchical success, and life success. A total of 20 items were used. Respondents rated each item on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Example items include: “I am receiving positive feedback about my performance,” “I am respected by my peers,” “I am earning as much as I think my work is worth,” and “I am satisfied with my life overall.” The items were translated into Thai using a translation and back-translation procedure to ensure linguistic and cultural equivalence. All items were subjected to confirmatory factor analysis. Reliability and validity were satisfactory across the five dimensions. Although a term related to “work commitment” appears in more than one dimension in the original scale, discriminant validity was confirmed using both the Fornell–Larcker criterion and HTMT values, indicating that no problematic redundancy or collinearity existed between dimensions.

Four motivational variables were measured using single-item indicators. Positive reinforcement and negative punishment were coded as binary variables indicating whether respondents experienced encouragement or discouragement when registering for courses (0 = no, 1 = yes). Self-directed lifelong learning interest was also measured dichotomously (0 = not self-interested, 1 = self-interested). Willingness to engage in classes was assessed using a continuous 10-point scale reflecting respondents’ overall intention to participate in lifelong learning. Because these indicators represent single item observed measures rather than multi-item latent constructs, reliability coefficients such as Cronbach’s alpha and composite reliability were not applicable. The use of single-item indicators is considered appropriate when constructs are concrete, unidimensional, and easily understood by respondents, particularly for behavioral intentions and clearly defined motivational conditions (Wanous et al., 1997; Bergkvist and Rossiter, 2007). For the binary indicators, internal consistency measures are conceptually inappropriate, as these variables represent observed motivational conditions rather than reflective scales.

All items were reviewed by two experts in adult learning, and pilot testing confirmed that the wording was clear and aligned with the Thai learning context. Finally, demographic variables (gender, generation, occupation, income, and education) and household head status were included as predictors.

The survey was administered online through Chiang Mai University’s Lifelong Learning Platform between March and July 2024. The target population comprised adult learners who had participated in at least three short courses offered by the university within the past year. This inclusion criterion ensured that respondents had adequate exposure to the institution’s lifelong learning programs, allowing them to provide informed and reflective responses based on sustained learning experiences.

A purposive sampling method was employed, selecting individuals who had direct experience with lifelong learning activities. This approach was deemed appropriate given the study’s focus on understanding motivation and engagement among active lifelong learners, rather than the general population. A total of 1,200 invitations were distributed electronically to eligible participants, of which 446 completed responses were received, yielding a response rate of approximately 37.2%. While this response rate is within the acceptable range for online academic surveys, it is possible that individuals with stronger intrinsic motivation or more positive learning experiences were more likely to participate, which may have introduced self-selection bias. Additionally, because all respondents were drawn from a single university in Thailand and exclusively from short-course programs, the sample’s demographic characteristics, such as the higher share of government employees and bachelor’s degree holders, may not represent the broader national population of lifelong learners, who vary widely in age, occupation, socioeconomic background, and educational levels. The online survey format may also have favored participants with stronger digital access or higher motivation, further contributing to sampling bias. These limitations should be considered when interpreting the results and when applying them to other lifelong learning settings, such as community-based or non-formal programs. However, comparisons of early and late respondents revealed no significant differences in demographic variables or key constructs, suggesting that non-response bias was minimal.

Participation was strictly voluntary, anonymous, and confidential. Respondents provided informed consent before accessing the questionnaire and were informed that their data would be used solely for academic research. To protect privacy, no identifying information was collected, and responses were stored securely in encrypted databases. The complete questionnaire, including item wording and construct mapping, is provided in the Supplementary material.

Table 2 summarizes the respondents’ basic profiles, including gender, occupation, income, and educational attainment.

According to Table 2, the demographic data reveal important insights into the composition of lifelong learners. Females comprise the majority of the sample (52.47%), and Millennials constitute the largest generational group (43.95%). By contrast, Baby Boomers (born before 1965) account for only 2.91% of respondents, indicating that the program attracts a predominantly younger audience. Government employees form the largest occupational group (52.69%), followed by unemployed individuals (23.54%). These figures reflect the diverse professional backgrounds of lifelong learners. In addition, income levels vary considerably, with 28.25% of participants earning less than THB 20,000 per month, while 26.91% report monthly incomes exceeding THB 50,000.

In terms of educational attainment, 46.19% of respondents have completed a bachelor’s degree, while smaller proportions hold a master’s degree (27.13%) or a doctoral degree (15.02%). A substantial 67.04% of participants identify themselves as heads of their households, highlighting the significant responsibilities many learners carry alongside their educational engagement. Regarding motivation, personal interest emerges as the primary reason for enrolling in lifelong learning courses, with 87.44% of respondents indicating that self-driven motivation is their main reason for registration. In contrast, external reinforcement, such as encouragement from others, plays a limited role, as 65.70% of participants report receiving no such reinforcement. Willingness to engage in lifelong learning is notably high, with an average score of 9.16 out of 10 on the intention-to-engage scale.

Table 3 presents the summary statistics for all variables, illustrating the overall variation and distribution patterns across the sample.

Table 3 also shows considerable variation in both career success indicators and motivational factors. Life success ( CS _ 5 ) records the highest average score, whereas financial success ( CS _ 3 ) displays the greatest dispersion, indicating diverse economic perceptions among learners. Demographic variables show balanced gender and generation distributions, while motivation-related items reveal particularly high willingness to engage in lifelong learning.

According to Table 4, the SEM reveals that career success was significantly and positively associated with five key subdimensions: job success, interpersonal success, financial success, hierarchical success, and life success. Among these, financial success appears to show a relatively stronger loading, suggesting that many individuals may associate success with tangible financial outcomes such as income, savings, or financial stability. Similarly, hierarchical success and interpersonal success also show strong positive loadings, indicating that upward career mobility and the ability to maintain positive professional relationships may play an important role in how individuals conceptualize career success. Job success is another significant dimension, though slightly less influential, reflecting the importance of personal achievement and satisfaction within one’s occupational role. Lastly, life success demonstrats a comparatively lower but still meaningful association, implying that while career accomplishments contribute to overall life satisfaction, this dimension may be more peripheral or indirectly influenced.

The measurement model further confirms adequate reliability and convergent validity, with Cronbach’s Alpha (0.884) and Composite Reliability (0.887) both exceeding the 0.70 threshold, and an Average Variance Extracted (AVE) value of 0.614 surpassing the 0.50 benchmark (Hair et al., 2021). These results indicate that the Career Success construction is statistically robust and suitable for inclusion in the structural model.

Then, we analyze the impact of motivational academic engagement on the effectiveness of lifelong learning. As shown in Table 5, the standardized path coefficients summarize the direct effects of sociodemographic and motivational variables on the five dimensions of career success. The first column presents the overall pattern, followed by results for job, interpersonal, financial, hierarchical, and life success.

The results indicate that three out of four motivational variables significantly affect career success. The negative association between negative punishment and career success is consistent with prior studies (e.g., Ge et al., 2022; Wahyudi, 2022). In contrast, the negative effect of positive reinforcement is unexpected and counterintuitive. Traditionally, positive reinforcement is theorized to enhance performance and goal attainment by encouraging the repetition of desirable behaviors (Cameron and Pierce, 1994). Moreover, Deci and Ryan (2000) argued that when reinforcement is perceived as supportive rather than controlling, it can strengthen intrinsic motivation and contribute to long-term success. However, in the present study, positive reinforcement is associated with a small negative coefficient (−0.170), suggesting a potential inverse relationship, although the effect size remains modest.

One possible explanation is that, in adult or professional learning contexts, external rewards or praise may be perceived as superficial or misaligned with learners’ self-directed goals. This interpretation is consistent with the findings of Atak et al. (2016), who reported that individuals with strong career direction and goal clarity tend to be less responsive to externally imposed motivators. Similarly, Niati et al. (2021) found that adult learners respond more favorably to motivation arising from their own intentions than to generalized rewards or praise provided by others. These results can be further interpreted through established motivational frameworks. According to Knowles’ theory of andragogy, adult learners are primarily self-directed and rely heavily on intrinsic motivation, implying that externally imposed reinforcement may, under certain conditions, undermine autonomy and engagement (Knowles, 1970). This interpretation aligns with the concept of motivational crowding out, which suggests that extrinsic rewards can displace intrinsic motivation when they are perceived as controlling rather than autonomy-supportive (Deci and Ryan, 2000; Frey and Jegen, 2001). Consistent with self-determination theory, autonomy-supportive reinforcement is more likely to foster competence, persistence, and long-term satisfaction, whereas controlling reinforcement may weaken intrinsic motivation and ultimately reduce career-related outcomes (Ryan and Deci, 2017).

Another plausible explanation relates to the misalignment between reinforcement and outcome expectations. Learners who receive frequent praise may develop elevated expectations regarding career advancement; when such expectations are not realized, their satisfaction and self-assessment of success may decline. This interpretation is consistent with the argument advanced by Arthur et al. (2005), who emphasized that boundaryless careers increasingly require adaptability and proactive agency rather than passive encouragement. Taken together, these findings suggest that the effectiveness of motivational strategies varies by context and perceived authenticity. Positive reinforcement may therefore yield beneficial effects only when it is timely, personalized, and autonomy-supportive, rather than formulaic or detached from learners’ goals (Evers et al., 1998; Boeren, 2016). In contrast, the negative coefficient associated with reduced feedback may indicate that insufficient guidance weakens goal focus and professional discipline, consistent with earlier findings highlighting the role of feedback in adult learning contexts (Niati et al., 2021; Ge et al., 2022).

Regarding intention to engage, the results show a positive and statistically significant association with career success. Individuals who express a stronger intention to participate in lifelong learning are more likely to perceive themselves as progressing in their careers. This finding underscores the importance of proactive learning orientation in professional development. It is consistent with prior research by Atak et al. (2016), who found that perceptions of career growth and employability are closely linked to individuals’ motivation to participate in training. Similarly, Boeren (2016) and Drewery et al. (2020) emphasized that adults who actively seek learning opportunities tend to exhibit higher levels of self-regulation, goal orientation, and career adaptability.

With respect to demographic factors, the results indicate that female learners report significantly higher levels of perceived career success than LGBTQIA+ individuals, with a mean difference of 0.156 points. In terms of occupational status, unemployed individuals report lower career success than those who are self-employed, with a significant gap of 0.184 points. Although statistically significant, these effect sizes remain relatively modest. In addition, learners pursuing short courses alongside higher education degrees (bachelor’s, master’s, or doctoral levels) tend to report lower career success than those holding secondary or vocational qualifications. This finding contrasts with earlier evidence suggesting that higher educational attainment is generally associated with improved career outcomes (Judge et al., 1995) and may reflect contextual differences in labor market conditions or expectation–reality gaps among highly educated learners engaged in short-course programs.

To assess the robustness of the main findings, additional analyses are conducted by regressing each dimension of career success (CS₁–CS₅) on the motivational variables. The results largely confirm the stability of the primary model. Positive reinforcement and negative punishment consistently exhibit negative associations across several career success dimensions, particularly job success (CS₁) and financial success (CS₃). In contrast, intention to engage maintains a positive and statistically significant relationship across all five dimensions, with the strongest association observed for CS₅. These results reinforce the central role of proactive engagement in shaping both professional and personal dimensions of perceived career success. The control condition, however, is significant only for job success (CS₁), suggesting a more limited and context-specific influence.

As shown in Table 6, the explanatory power (F²) of most sociodemographic and motivational variables is small, indicating modest individual contributions to career success. However, intention to engage (f² = 0.067) exhibits the strongest effect among motivational constructs. The R² values, ranging from 0.095 to 0.183, confirm a moderate level of explained variance.

Figure 2 presents the structural model estimated through bootstrapping procedures in the PLS-SEM framework. Unlike Table 5, which reports the original sample estimates of path coefficients, this figure displays the empirical results derived from 5,000 bootstrap resamples, providing a more reliable assessment of the model’s stability and significance. Minor variations between the original and bootstrapped coefficients are expected, as the latter represent averaged estimates obtained from repeated resampling. The consistent significance levels across most structural paths confirm the robustness of the proposed model and indicate that the relationships among motivation, sociodemographic factors, and career success are statistically stable. Therefore, the bootstrapping validation supports the reliability of the PLS-SEM estimation and strengthens the empirical credibility of the study’s findings.

Table 7 presents the direct effects of demographic characteristics on the four motivational constructs, forming the first stage of the mediation analysis.

The direct effects of demographic characteristics on motivation and engagement indicate substantial variation in motivational patterns across learner groups. Gender differences emerge in specific forms of motivation. Female learners appear to respond more strongly to positive reinforcement, while at the same time exhibiting lower willingness to engage in learning activities. This pattern suggests that although external encouragement may be salient for female learners, other factors, such as structural constraints or concerns related to career advancement, particularly in male-dominated fields, may still limit their readiness to participate more actively (Ng et al., 2005). Male learners, by contrast, display a more neutral motivational profile, which is broadly consistent with previous findings indicating higher self-efficacy and performance-oriented attitudes among men in adult learning contexts (Niati et al., 2021; Wahyudi, 2022).

Generational differences are also evident. Gen X and Gen Z learners show lower responsiveness to external motivators but higher willingness to engage in learning activities, suggesting a pattern more closely aligned with intrinsic or purpose-driven engagement. This is consistent with earlier research indicating that mid-career and younger adults often pursue learning for skill renewal, adaptation, or personal development (Boeren, 2016). In contrast, Millennials exhibit greater sensitivity to positive reinforcement alongside lower willingness to engage, pointing to a possible disconnect between external encouragement and actual learning intention. Such a pattern may reflect greater reliance on external validation or unmet expectations within lifelong learning environments, as suggested by Fajčíková and Urbancová (2017). Occupational status also contributes to divergent motivational tendencies. Freelancers exhibit a clearer inclination to engage in learning, consistent with the flexible, performance-driven, and uncertainty-prone nature of freelance work, where proactive behavior and adaptability are particularly important (Crant, 2000; Arthur et al., 2005). Conversely, government employees show motivational patterns consistent with compliance-oriented environments, characterized by stronger responsiveness to negative consequences and weaker voluntary engagement. This aligns with prior findings suggesting that rigid institutional structures may reduce the perceived need for personal initiative and learning autonomy (Field, 2000; Crocco, 2018).

Income and education levels reveal additional distinctions. Middle-income earners, particularly those facing greater work or financial pressures, tend to show reduced willingness to engage and less favorable learning conditions. In contrast, higher-income individuals exhibit relatively stable motivational patterns, possibly reflecting greater flexibility and fewer external constraints. Among educational groups, bachelor’s and master’s degree holders show lower willingness to participate in additional learning, whereas doctoral graduates demonstrate stronger motivational readiness, consistent with their longer-term orientation toward academic and professional development (Gattiker and Larwood, 1988; Drewery et al., 2020). Finally, household heads show reduced willingness to engage in learning activities, reflecting the reality that caregiving responsibilities, time constraints, and competing demands often limit engagement opportunities. Although they may value external encouragement, their capacity to translate motivation into action may be constrained by family pressures, consistent with evidence on how household responsibilities shape adult learning participation (Florin et al., 2020).

Table 8 presents the indirect effects of sociodemographic characteristics on career success through four motivational pathways: positive reinforcement, negative punishment, intention to engage, and condition. The results support the study’s hypothesis that motivation functions as a significant mediator, although the strength and direction of these indirect effects vary across demographic profiles. Gender shows consistent and significant mediation effects. Specifically, female learners experience a negative indirect effect on career success through both positive reinforcement and intention to engage, suggesting that lower levels of motivational engagement among women may limit their perceived career advancement. In contrast, male learners demonstrate positive indirect effects through the same pathways, indicating that gender differences in motivational experiences partially explain differences in career outcomes. These findings are consistent with prior research by Ng et al. (2005) and Wahyudi (2022).

Generational differences reveal distinct patterns. Millennials benefit indirectly through positive reinforcement but are negatively influenced by intention to engage, reflecting a potential mismatch between external motivation and internal learning intentions. Gen X and Gen Z also show significant but smaller indirect effects, reinforcing earlier findings that motivation is shaped by generational learning attitudes (Boeren, 2016; Brady et al., 2024). Occupational status also plays an important role. Freelancers exhibit strong positive indirect effects across all motivational pathways, particularly through intention to engage, suggesting that they actively leverage learning as a tool for career mobility. In contrast, government employees show significant negative mediation through negative punishment and willingness, implying lower levels of learning engagement, possibly related to more rigid career structures and weaker performance accountability (Crocco, 2018). In terms of income, the results indicate that individuals in lower income brackets (e.g., 20–30 k baht) experience positive indirect effects, particularly through intention to engage, highlighting how economic pressure may motivate learning engagement. Conversely, higher-income learners (Income₄) display weaker or slightly negative indirect effects via motivation, suggesting lower urgency or reduced perceived benefit from short-course participation (Arthur et al., 2005). Educational level also influences motivation-driven success. Doctoral-level participants show a small but significant negative indirect effect through positive reinforcement, indicating possible dissatisfaction or unmet expectations from the learning experience. This pattern is consistent with findings by Drewery et al. (2020), which suggest that overqualified individuals may require more specialized or challenging content to remain engaged.

Finally, individuals who identify as household heads report significant negative indirect effects through willingness, despite slightly positive effects through reinforcement. This pattern may reflect the practical constraints of balancing family responsibilities with personal development goals (Florin et al., 2020).

Table 9 presents the decomposition of the direct, indirect, and total effects of sociodemographic characteristics on career success, incorporating all four motivational constructs as simultaneous mediators. The results show that demographic variables influence career success through both direct and mediated pathways, although the indirect effects via positive reinforcement, negative punishment, learning conditions, and willingness to engage are generally stronger and more consistent. In most cases, direct effects are weak or nonsignificant, whereas the significant indirect effects indicate that motivation serves as an important mechanism through which demographic background is associated with career outcomes. Freelancers and some lower-income groups benefit from substantial positive mediation through motivation, whereas government employees and higher-educated individuals exhibit negative overall effects driven largely by their direct associations with career success.