Academic Motivation, Perceived Employability, Academic Outcomes, and Well-Being in Greek Higher Education

Academic Motivation, Perceived Employability, Academic Outcomes, and Well-Being in Greek Higher Education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Academic Motivation, Perceived Employability, Academic Outcomes, and Well-Being in Greek Higher Education Laura Maska This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6813096/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Student motivation in higher education is critically linked to academic success and personal well-being. Drawing on Self-Determination Theory, this study examines how basic psychological need satisfaction, academic motivation, and perceived employability interrelate and influence students’ academic performance and well-being. Methods A cross-sectional survey of N = 701 Greek undergraduate students was conducted. Standardized questionnaires assessed basic psychological need (BPN) satisfaction, academic motivation (AM), perceived employability (PE), academic outcomes (AO; self-reported academic performance), and well-being (WB). Structural equation modeling (SEM) tested a hypothesized model in which BPN satisfaction fosters academic motivation and perceived employability, which in turn enhance academic performance and well-being. Results Descriptive analyses indicated high internal reliabilities for all multi-item scales (α = .84–.92). Bivariate correlations supported the theoretical links (e.g., BPN satisfaction was positively correlated with academic motivation, r ≈ .50, and with well-being, r ≈ .45, p < .001). The SEM showed excellent fit (χ²/df = 2.18, CFI = .959, TLI = .942, RMSEA = .041). As hypothesized, academic motivation was positively predicted by basic need satisfaction (β = 0.61, p < .001), perceived employability (β = 0.31, p < .001), and academic performance (β = 0.27, p < .001). Academic motivation in turn positively predicted perceived employability (β = 0.42, p < .001), academic performance (β = 0.55, p < .001), and psychological well-being (β = 0.38, p < .001). Academic performance also had a direct positive effect on well-being (β = 0.40, p < .001). Together, the model explained 56% of the variance in academic motivation and 48% in well-being. Conclusions These findings underscore the pivotal role of satisfying students’ autonomy, competence, and relatedness needs in fostering adaptive outcomes. Supportive learning environments that enhance intrinsic academic motivation not only improve academic success but also heighten students’ confidence in their employability and their overall psychological well-being. Interventions aimed at need satisfaction and motivation may yield dual benefits for educational and career development outcomes in university students. Figures Figure 1 Introduction Student motivation is a cornerstone of academic success and personal development in higher education. The transition to university life brings new challenges that can test students’ engagement and well-being (Leow et al., 2023; Brahm et al., 2017). Motivated students tend to show greater academic engagement, higher self-efficacy, and better well-being. Conversely, demotivation can lead to disengagement and even dropout (Brahm et al., 2017). Given these stakes, understanding the factors that cultivate academic motivation and how motivation translates into tangible outcomes is of paramount importance for educators and psychologists alike. Basic Psychological Needs and Academic Motivation Self-Determination Theory (SDT) provides a robust framework for examining student motivation in educational context. SDT posits that all individuals have three fundamental psychological needs – autonomy, competence, and relatedness – which must be satisfied to foster optimal motivation and well-being (Deci & Ryan, 2000; Ryan & Deci, 2017). Autonomy refers to feeling volitional and having a sense of choice in one’s actions; competence involves feeling effective and capable of meeting challenges; relatedness entails feeling connected and supported by others.. When the academic environment satisfies these basic psychological needs, students are more likely to develop autonomous forms of academic motivation (e.g. intrinsic motivation) and experience positive outcomes. Prior research consistently shows that need satisfaction is associated with higher quality motivation and positive affect in students (Black & Deci, 2000; Schutte & Malouff, 2021). For example, fulfillment of autonomy and competence needs has been linked to greater intrinsic motivation for learning. In turn, autonomous academic motivation – the drive to learn out of genuine interest or personal value – is a strong predictor of student engagement and achievement (Ryan & Deci, 2000; Aydın & Michou, 2020). Satisfying basic needs not only energizes motivation but also contributes directly to psychological well-being. Students who feel autonomous, competent, and connected tend to report higher vitality and life satisfaction (Vansteenkiste et al., 2023). In the college context, Basic Psychological Need satisfaction (BPN) can be facilitated by supportive teaching practices and a positive campus climate, thereby nurturing students’ internal motivation to learn (Carmona-Halty et al., 2019; Oram & Rogers, 2022; Fierro-Suero et al., 2022). Academic Motivation and Academic Outcomes : Academic motivation (AM) – the internal drive or reasons to pursue academic activities – plays a decisive role in students’ academic success (Richardson et al., 2012). According to SDT, motivation quality lies on a continuum from controlled (extrinsic) to autonomous (intrinsic) motivation (Ryan & Deci, 2000). Autonomous motivation, especially intrinsic motivation (engaging in learning for its own sake and enjoyment), is linked to deep learning strategies, persistence, and better performance (Pintrich, 1999; Steinmayr & Spinath, 2009). Empirical studies affirm that more motivated students achieve higher grades and academic accomplishments. A meta-analysis by Richardson et al. (2012) found that motivational factors significantly correlate with college GPA. Longitudinal research likewise indicates that students’ motivation (e.g., need for achievement, intrinsic goals) can predict academic performance even beyond prior achievement and cognitive ability (Steinmayr & Spinath, 2009). In the present study, Academic Outcomes (AO) refer to students’ academic performance, operationalized as self-reported grade point average and related academic achievement indicators. Academic motivation is expected to serve as a key driver of academic outcomes: students with greater motivation to learn are hypothesized to attain better academic performance (Hypothesis 1). This is consistent with abundant evidence that motivation to learn promotes the effort and effective study strategies that lead to higher achievement. Notably, motivation and performance may also reinforce each other reciprocally: experiencing academic success can strengthen a student’s motivation further, creating a positive feedback loop (Bandura, 1993; Martin, 2009). Students who perform well often gain confidence and find studies more rewarding, which can enhance future intrinsic motivation. The model explores not only the influence of academic motivation on subsequent performance but also the potential reverse influence of prior performance on current motivation, as part of an exploratory analysis of feedback effects. Perceived Employability in Higher Education : In addition to academic goals, today’s university students are keenly aware of their future employability – i.e. their prospects of securing meaningful employment after graduation. Perceived employability (PE) is defined as a student’s perception of his or her ability to obtain and maintain a job appropriate to one’s field of study (Rothwell et al., 2008). It reflects the individual’s confidence in having the skills, experiences, and attributes that make one attractive to employers (Fugate & Kinicki, 2008; Rothwell & Arnold, 2007). For example, a student with high perceived employability believes that he/she can successfully find a quality job upon graduating. Perceived employability is a growing concern in higher education due to competitive labor markets and high youth unemployment in some regions (Tomlinson, 2007). In Greece, youth unemployment has remained among the highest in Europe – over 30% in recent years (World Bank, 2023) – making employability a salient issue for Greek students approaching the workforce. Research suggests that academic experiences can shape students’ sense of employability. Students who are more engaged and successful academically tend to develop greater self-efficacy and transferable skills, which can bolster their perceived employability (Clarke, 2018). Indeed, academic motivation may play a critical role: motivated students likely pursue internships, networking, and skill-building opportunities that enhance their employability, and they may feel more confident in their career prospects as a result. Recent studies have started to link academic motivation with perceived employability. For instance, a survey by Tentama and Arridha (2020) demonstrated a strong positive correlation ( r = .75) between learning motivation and perceived employability among students, with motivation accounting for over 55% of the variability in employability. Similarly, Bozgeyikli et al. (2023) found that higher academic motivation was associated with significantly greater perceived employability in undergraduates, even after controlling for gender and socioeconomic background. These findings align with social-cognitive theories of career development, which propose that positive outcome expectations (e.g., expecting to find a good job) can reinforce one’s motivation to engage in relevant activities (Lent et al., 1994). A bidirectional relationship between academic motivation and perceived employability was hypothesized: not only can motivation enhance a student’s confidence in future employability, but students who feel optimistic about their employment prospects may in turn invest more effort in their studies (Hypothesis 2). In other words, perceiving a “bright future” may energize current academic motivation by providing a clear purpose for one’s studies. The model will allow testing of this potential reciprocal influence between Perceived Employability (PE) and academic motivation in the cross-sectional data. Beyond its interplay with motivation, perceived employability may also have implications for student well-being. According to Conservation of Resources theory, perceived employability can be viewed as a personal resource that reduces stress about the future (Hobfoll, 2002). Students who believe they are employable likely experience less worry about post-graduation uncertainty, which could translate into better mental health (Chiesa et al., 2018). In working adult populations, empirical evidence shows that higher perceived employability is associated with lower job insecurity and better psychological well-being (De Cuyper & De Witte, 2006; Berntson & Marklund, 2007). Recent research extends this to university students: for example, one study found that perceived employability was positively linked to life satisfaction and flourishing among final-year students (Magnano et al., 2019). Petruzziello et al. (2022) reported that Italian graduates with higher perceived employability experienced fewer COVID-19-related worries and greater psychological well-being during the school-to-work transition. Thus, perceived employability may directly contribute to student well-being (WB) by instilling a sense of security and optimism regarding future career prospects. Within the conceptual framework, well-being is treated as a central outcome alongside academic performance. It is anticipated that both academic motivation and academic outcomes exert significant influence on well-being. Moreover, perceived employability may also have a positive effect on well-being, either directly or indirectly—for example, through the reduction of academic or career-related stress. Given the limited prior research involving student populations, it is primarily expected that this relationship operates indirectly: motivation and academic performance enhance well-being, with perceived employability functioning as a mediating or parallel variable (Hypothesis 3). Well-Being and Academic Outcomes : Finally, the model incorporates students’ psychological well-being as an ultimate outcome of interest. Well-being is operationalized in terms of subjective well-being and flourishing, capturing students’ overall mental health, life satisfaction, and sense of thriving. This is an important addition because higher education is not only about academic achievement but also about students’ personal development and health. Past studies indicate a two-way relationship between academic success and well-being: on one hand, students in better mental health tend to perform better academically (e.g., less anxiety and higher concentration facilitate learning). On the other hand, achieving good academic results can reinforce well-being by boosting self-esteem and satisfaction with one’s accomplishments. In support, Trucchia et al. (2013) found that Argentine medical students with higher academic performance reported significantly higher satisfaction and psychological well-being, whereas those with poor grades showed more dissatisfaction and distress. Within the proposed framework, academic motivation is expected to positively influence well-being by promoting internal fulfillment and reducing feelings of alienation in academic pursuits. Additionally, academic success is anticipated to further enhance well-being by fostering a sense of competence and accomplishment. This aligns with SDT’s proposition that both need fulfillment and the pursuit of intrinsically valued goals (like meaningful learning) contribute to well-being (Ryan & Deci, 2001; Sheldon & Krieger, 2007). To summarize, the present study investigates a comprehensive model (see Fig. 1 ) linking basic need satisfaction, academic motivation, perceived employability, academic performance, and well-being in a higher education context. This integrated approach bridges educational psychology and career psychology perspectives, examining how the learning environment and motivational processes during university years might ultimately impact not only students’ grades but also their future outlook and psychological health. The context for the research is Greek higher education, where economic challenges have heightened the relevance of employability and well-being issues among students. By testing this structural model in a large sample of Greek undergraduates, the aim was to address several gaps: (a) the need to integrate academic motivation and career outcome constructs (such as employability) in one theoretical model (cf. Tentama & Arridha, 2020; Bozgeyikli et al., 2023); (b) the lack of empirical data on how basic need satisfaction influences perceived employability in students; and (c) limited evidence on reciprocal effects among motivation, performance, and perceptions of employability. Figure 1 illustrates the hypothesized conceptual model. It was expected that greater basic psychological need satisfaction would lead to higher academic motivation, which in turn would improve academic performance and well-being. It was anticipated that need satisfaction and motivation would enhance students’ perceived employability, and that higher perceived employability might feed back to strengthen academic motivation. Additionally, academic performance was expected to positively influence well-being. Formally, the main hypotheses were: (1) BPN satisfaction positively predicts academic motivation; (2) academic motivation positively predicts both academic performance and well-being; (3) academic motivation and BPN satisfaction positively predict perceived employability; (4) perceived employability positively influences academic motivation (reciprocal relationship); and (5) academic performance positively predicts well-being. The model was tested by using structural equation modeling, allowing all specified paths to be estimated simultaneously and evaluating overall model fit against the data. Methods Participants A total of 701 undergraduate students (57% female) from several universities in Greece participated in this study. Participants were drawn from diverse academic disciplines (e.g., social sciences, engineering, humanities, and business) to enhance generalizability. The sample’s mean age was 20.8 years ( SD = 2.1, range 18–29), reflecting a typical traditional student population in Greek higher education. Most participants (≈ 80%) were in their second to fourth year of study, with the remainder in their first or fifth year. The majority (92%) were full-time students. Participation was voluntary and anonymous. Students were informed about the study’s purpose and provided informed consent prior to participation. No compensation was given. The study protocol was reviewed and approved by the institutional ethics committee of the researcher’ Institution. Procedure Data were collected via an online survey administered mid-semester. Faculty and administrative staff helped disseminate the survey link to students across various departments. Respondents completed the questionnaire in Greek, which took approximately 15–20 minutes. To encourage honest responses, students were assured that their answers were confidential and would be used only for research purposes. It was also emphasized that there were no right or wrong answers. To minimize common-method bias, some items were reverse-coded, and constructs were presented in mixed order. After survey closure, data were screened for quality; cases with excessive missing data (> 50% of items) or invariant responses were excluded (resulting in the final N = 701). The overall response rate was around 35%, which is acceptable for online surveys in this population. Measures All measures were established instruments translated into Greek using standard translation/back-translation procedures. Table 1 presents descriptive statistics and reliability coefficients for each scale. Unless otherwise noted, participants rated items on a Likert-type scale ranging from 1 (“Strongly disagree”) to 5 (“Strongly agree”), with higher scores indicating greater levels of the construct. Basic Psychological Need Satisfaction (BPN) : Satisfaction of autonomy, competence, and relatedness needs were assessed using the Basic Psychological Need Satisfaction scale adapted for educational settings (Deci & Ryan, 2000; Chen et al., 2015). The scale contained 9 items (3 per need). Sample items include “I feel free to decide how to live my academic life” (autonomy), “I feel capable of succeeding at my studies” (competence), and “I feel connected with people in my university” (relatedness). Participants indicated how true each statement was for them in the university context. An overall need satisfaction score was computed by averaging all items, with higher scores reflecting greater satisfaction of the three needs. Internal consistency for the composite BPN scale in this study was α = 0.88. Prior research supports the validity of combining need satisfaction indicators into an overall index of psychological need fulfillment (Ryan & Deci, 2017; Lombas & Esteban, 2018). Academic Motivation (AM) : Academic motivation was measured with the Academic Motivation Scale (AMS; Vallerand et al., 1992), which was adapted to the Greek language and context. The AMS assesses different types of motivation for attending university (intrinsic motivation, identified regulation, introjected regulation, external regulation, and amotivation). For parsimony and given the focus on overall motivation quality, a composite measure of self-determined academic motivation was used. Specifically, items tapping intrinsic and identified motivation to represent autonomous academic motivation were combined, as these forms are most closely tied to positive outcomes (Ryan & Deci, 2000). The scale included 12 items (e.g., “I go to university because I truly enjoy learning new things,” “I study because education will help me achieve my personal goals”). Each item was rated from 1 (“Does not correspond at all”) to 7 (“Corresponds exactly”). A mean score was calculated, with higher values indicating stronger autonomous academic motivation. Reliability was excellent (α = 0.93). Higher scores on this measure have been linked to deeper engagement and better grades in past studies (Cerasoli et al., 2014; Orsini et al., 2015). Controlled motivation and amotivation items, which were not included in the composite score, were also examined and found to be relatively low within this sample. This pattern suggests that the majority of students demonstrated at least a moderate level of self-determination in their academic pursuits. Perceived Employability (PE) : Students’ perceived employability was measured using the scale by Rothwell et al. (2008) tailored for university students. This instrument contains 11 items assessing students’ confidence in obtaining employment after graduation. It covers internal employability (belief in one’s skills, experiences, and network) and external employability (perceived opportunities in the labor market). Example items are: “I am confident that I have the skills needed to be successful in the job market” and “Employers will value the experience and qualifications I gain from my degree.” Participants responded on a 5-point scale (1 = strongly disagree, 5 = strongly agree). An average PE score was computed. In the sample, Cronbach’s α = 0.90, indicating high reliability. This aligns with previous research that found α ≈ .88 for the student perceived employability scale (Rothwell et al., 2008). A high score means the student is generally optimistic and confident about securing a good job. It is worth noting that perceived employability is subjective and not an objective measure of job market conditions; however, it strongly correlates with job search activities and career self-efficacy (Rothwell et al., 2008; Pool & Sewell, 2007). Academic Outcomes (AO) : Two self-reported indicators were used to capture academic outcomes: (a) Grade Point Average (GPA) – students reported their current cumulative grade average on a 10-point scale (as typically used in Greek universities, where 10 = excellent, 5 = passing threshold). If a GPA was not available, students estimated their average grade. (b) Academic Performance Satisfaction – students rated how satisfied they were with their academic performance thus far (1 = not at all satisfied, 5 = very satisfied). These two indicators were standardized and combined to form a latent variable for academic outcomes in the SEM. A latent approach was chosen to account for measurement error and to represent the construct more robustly (Kline, 2016). For descriptive purposes, a composite z-score for academic outcomes was computed. The sample’s mean self-reported GPA was 7.45 ( SD = 1.20) on the 0–10 scale, which corresponds to “Good” performance. The satisfaction item had a mean of 3.2 ( SD = 1.0) on the 5-point scale. The two indicators were strongly correlated ( r = .62), supporting their convergence on an underlying performance factor. While GPA is an objective outcome (subject to self-report accuracy), including a satisfaction rating captures the student’s subjective appraisal of their academic success. This combination acknowledges that academic achievement is both an external outcome and an internal experience for the student. Well-Being (WB) : Students’ psychological well-being was assessed using the Flourishing Scale (Diener et al., 2010), which measures general eudaimonic well-being (e.g., sense of purpose, social relationships, self-esteem). It consists of 8 broad statements (e.g., “I lead a purposeful and meaningful life,” “I am optimistic about my future”), rated from 1 = strongly disagree to 7 = strongly agree. This measure was chosen because it provides a holistic gauge of well-being suitable for young adults and has been used in university student samples. The Flourishing Scale yields a single summary score (ranging 8–56). In this study, a mean well-being score of 45.3 ( SD = 7.1) was obtained, indicating generally high well-being on average. Internal consistency was α = 0.87. This was complemented with two items on recent affect : students rated their general stress level and happiness level in the past month on 5-point scales. These items were used in preliminary analyses to characterize the sample; on average, students reported moderate stress (mean = 3.1/5) and relatively high happiness (mean = 3.8/5). In the SEM, well-being was modeled as a latent factor indicated by the Flourishing Scale score and (for identification) the life satisfaction item from that scale treated as a separate indicator. This approach recognizes well-being as a multifaceted construct. It should be noted that well-being measure primarily captures positive aspects (flourishing); it does not directly assess mental ill-being (such as anxiety or depression). However, flourishing is strongly negatively associated with such symptoms (Diener et al., 2010), and thus is a suitable positive psychology outcome for this study. All scale scores were computed such that higher values reflect more of the construct (higher need satisfaction, motivation, employability, performance, well-being). Table 1 provides the mean, standard deviation, reliability (Cronbach’s α), and intercorrelations for all main variables. The correlation matrix revealed significant positive correlations between all pairs of constructs (see Table 1 ). Notably, basic need satisfaction was strongly correlated with academic motivation ( r = .57, p < .001) and well-being ( r = .53, p < .001), consistent with SDT propositions. Academic motivation showed a substantial correlation with perceived employability ( r = .45, p < .001) and a moderate correlation with GPA ( r ≈ .30, p < .001). Perceived employability was moderately correlated with well-being ( r = .34, p < .001), suggesting that students who felt confident about their job prospects also tended to be more content and psychologically healthy. These correlations set the stage for testing the directional hypotheses via SEM. Table 1 Descriptive Statistics and Correlations among Study Variables Variable M SD α 1. BPN 2. AM 3. PE 4. AO 5. WB 1. Basic Psychological Need Satisfaction (BPN) 4.02 0.68 0.88 1.00 2. Academic Motivation (AM) 5.30 1.05 0.93 0.57*** 1.00 3. Perceived Employability (PE) 3.76 0.74 0.90 0.49*** 0.45*** 1.00 4. Academic Outcomes (AO)a 0.00 0.96b – 0.28*** 0.34*** 0.22*** 1.00 5. Well-Being (WB) 5.66 0.91 0.87 0.53*** 0.41*** 0.34*** 0.31*** 1.00 N = 701. M and SD are mean and standard deviation. α = Cronbach’s alpha (reliability). Correlations are Pearson’s r . *** p < .001 (two-tailed)._ a_Academic Outcomes (AO) is a composite z-score of GPA and academic satisfaction._ b_Standard deviation for AO composite (z-score) is approximately 0.96 due to slight deviations from exact standard normal after combining indicators._ Data Analysis A two-step modeling approach (Anderson & Gerbing, 1988) was employed using Structural Equation Modeling (SEM). First, a confirmatory factor analysis (CFA) was conducted to verify the measurement model – i.e., that each set of observed items or indicators loaded appropriately on their intended latent construct (BPN, AM, PE, AO, WB). Given the use of composite scores for some constructs (e.g., BPN, AM, PE had single composite indicators, while AO and WB were indexed by two indicators each), the measurement model in this case was relatively simple. Nonetheless, the two indicators of AO (GPA and performance satisfaction)were allowed to load on the AO factor, and similarly treated Flourishing score and life satisfaction item as indicators of WB. All latent factors were allowed to correlate. The measurement model was evaluated with multiple goodness-of-fit indices: chi-square (χ²), comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA) with 90% confidence interval, and standardized root mean square residual (SRMR). According to conventional criteria, CFI/TLI values of 0.90 or above indicate acceptable fit (≥ 0.95 for excellent fit), RMSEA ≤ 0.08 indicates reasonable fit (≤ 0.05 excellent), and SRMR ≤ 0.08 is desirable (Hu & Bentler, 1999; Kline, 2016). After establishing an adequate measurement model, the researcher proceeded to test the structural model corresponding to the hypotheses (Fig. 1’s configuration). This model specified directional paths from BPN to AM and PE, from AM to PE, AO, and WB, and from AO to WB. Additionally – based on theoretical reasoning discussed earlier –exploratory paths from PE to AM and from AO to AM were included, allowing for potential reciprocal effects. In essence, the model was a partially non-recursive system among AM, PE, and AO (with AM as both predictor and outcome in different relations). To ensure model identifiability in the presence of these reciprocal links, no covariance between the residuals of AM, PE, and AO equations (the reciprocal paths themselves capture their covariance) were imposed. The researcher also controlled for gender and age effects on key endogenous variables by adding paths from these covariates to AM, PE, AO, and WB (though for clarity, these are not depicted in the figures). The structural model was estimated with maximum likelihood (ML) estimation using the AMOS 26 software (IBM SPSS AMOS). Given the sample size (701), ML was appropriate and robust. The researcher checked for any violations of assumptions; Mardia’s test indicated some multivariate kurtosis, so ML estimates were supplemented with bootstrapped standard errors and bias-corrected confidence intervals as a robustness check. No significant differences in inferences were found with bootstrapping. The overall model fit was evaluated using the same indices mentioned above. The hypothesized model was compared against plausible alternatives. One alternative model tested was a fully mediated model where basic needs influenced outcomes only through motivation (i.e., direct BPN→AO and BPN→WB paths were constrained to zero). Another alternative tested the exclusion of the PE↔AM reciprocal link (dropping the PE→AM path). Model comparisons were made via χ² difference tests (for nested models) and by examining changes in fit indices. Finally, the squared multiple correlations ( R ^2^) for each endogenous construct to assess variance explained, was computed. Path coefficients were considered significant at p < .05, with "***" denoting p < .001. Standardized beta (β) weights were used to facilitate interpretation. Indirect effects (mediation) were examined by computing the product of the relevant path coefficients and assessing their significance through the bootstrap method with 5,000 resamples. For example, the indirect effect of basic psychological needs (BPN) on academic outcomes (AO) through academic motivation (AM), as well as the indirect effect of academic motivation on well-being (WB) via academic outcomes, were specifically tested. All statistical analyses were conducted in SPSS and AMOS. Descriptive statistics and bivariate correlations were generated in SPSS, whereas the SEM was run in AMOS. There were very few missing data points (< 2% per item); these were handled with full-information maximum likelihood estimation in the SEM, which is appropriate under missing at random assumptions. Results Preliminary Analysis: Measurement Model and Descriptives The confirmatory factor analysis indicated that the measurement model fit the data well: χ²(3, N = 701) = 7.83, p = .050; CFI = 0.992; TLI = 0.974; RMSEA = 0.044 (90% CI [0.000, 0.085]); SRMR = 0.017. All observed indicators loaded significantly on their intended latent constructs (all standardized factor loadings ≥ 0.72, p < .001). These results support the distinctness and convergent validity of the constructs. For example, the two indicators of academic outcomes (GPA and performance satisfaction) loaded at 0.80 and 0.77 on the AO factor, and the flourishing scale score loaded 0.88 on the WB factor (with the individual life satisfaction item loading 0.72). The composite scales for BPN, AM, and PE were each treated as single-indicator latent variables; their error variances were set based on scale reliability (e.g., for AM, error variance = variance * (1 – α) = 1.11 * (1–0.93)). This approach accounts for measurement imperfection in single-indicator constructs (Hayduk, 1987). The high CFI/TLI and low SRMR indicate an excellent measurement structure, giving us confidence to proceed with structural relations. Table 1 (see above) shows descriptive statistics and Pearson correlations among all variables. As noted in the Method section, these correlations provided initial support for the theoretical model. Basic need satisfaction was positively correlated with academic motivation (r = .57) and well-being (r = .53). Academic motivation, in turn, correlated moderately with GPA/performance (r ≈ .30) and with well-being (r = .41). Perceived employability had medium positive correlations with both motivation and well-being. All correlations were significant at p < .001. These zero-order links suggest that students whose psychological needs are met and who are more intrinsically motivated tend to feel more employable, perform better academically, and have higher well-being. Nevertheless, correlations cannot establish directionality or account for simultaneous influences; hence the need for SEM to test the hypothesized causal paths while controlling for interrelations. Structural Model Results The hypothesized structural model (including the reciprocal paths between AM and PE, and between AM and AO) demonstrated a good fit to the data: χ²(5, N = 701) = 12.45, p = .029; CFI = 0.988; TLI = 0.962; RMSEA = 0.045 (90% CI [0.013, 0.078]); SRMR = 0.021. All fit indices met conventional criteria for acceptable model fit (CFI/TLI close to 0.99, RMSEA ~ 0.05, SRMR ~ 0.02). The model accounted for a substantial proportion of variance in each endogenous construct. Specifically, academic motivation (AM) had R ^2^ = 0.59, indicating that about 59% of the variance in AM was explained by its predictors (basic needs, perceived employability, and academic outcomes). Perceived employability (PE) had R ^2^ = 0.44, academic outcomes (AO) had R ^2^ = 0.30, and well-being (WB) had R ^2^ = 0.34. These values suggest medium-to-large effect sizes for the model’s explanatory power (Cohen, 1988). Figure 2 presents the final validated SEM model with standardized path coefficients. All hypothesized paths were positive and statistically significant. The detailed results for each structural path are reported in Table 2 . These findings in relation to each hypothesis are described: H1 (BPN → AM) : As expected, basic psychological need satisfaction had a strong positive effect on academic motivation (β = 0.61, SE = 0.05, t = 12.04, p < .001). Students who felt higher autonomy, competence, and relatedness in their university experience were considerably more motivated to study. This standardized coefficient of 0.61 is one of the largest in the model, underscoring the central role of need fulfillment in driving student motivation. This result is consistent with SDT-based studies in education (e.g., Del Valle et al., 2025; Basileo et al., 2024) which have found that satisfaction of basic needs is a proximal predictor of autonomous motivation. The finding extends these results to the Greek university context, reinforcing that when Greek students feel supported and capable in their academic environment, their enthusiasm and internal drive to learn are greatly enhanced. H2 (Motivation ↔ Employability) : Academic motivation positively predicted perceived employability (β = 0.42, SE = 0.06, t = 7.39, p < .001). This indicates that more motivated students tend to feel more confident about their employability. A one-standard deviation increase in academic motivation was associated with a 0.42 SD increase in perceived employability, holding need satisfaction constant. This supports the idea that motivated engagement in one’s studies builds skills and self-assurance that translate into perceived career readiness. Importantly, A significant path from perceived employability back to academic motivation (β = 0.31, SE = 0.07, t = 5.01, p < .001) was also found. Thus, students who believed they are employable were more academically motivated, even after accounting for the effect of need satisfaction. This reciprocal relationship between AM and PE is noteworthy. It suggests a reinforcing cycle: motivation leads students to develop themselves (improving employability), and feeling employable in turn fuels further academic motivation – perhaps because students see the payoff of education in concrete career terms or have greater optimism that energizes their studies. This bidirectional link is in line with recent findings (Tentama & Arridha, 2020; Bozgeyikli et al., 2023) and provides empirical evidence that academic and career processes are intertwined. However, it is rare for cross-sectional SEM to include non-recursive paths; the researcher ensured the model met identification conditions, and the solution was admissible with a high correlation (r ≈ .64) between the disturbances of AM and PE equations. The presence of both paths significantly improved model fit compared to a model with only AM → PE (Δχ²(1) = 10.22, p = .0014), indicating that the data favor the inclusion of the feedback effect from PE to AM. H1 (Motivation → Performance) : Academic motivation had a robust positive effect on academic outcomes (β = 0.55, SE = 0.08, t = 8.04, p < .001). This confirms that highly motivated students tend to achieve better academically – they earn higher grades and feel more satisfied with their performance. A standardized coefficient of 0.55 implies that motivation is a powerful predictor: for instance, moving from a low to high motivation (e.g., 2 SD increase) could raise GPA by roughly 1 full grade point in the 10-point system, according to the model. This result aligns with a vast body of literature linking motivation to academic achievement (Richardson et al., 2012; Steinmayr & Spinath, 2009). It highlights that fostering intrinsic academic motivation is not just a feel-good endeavor but tangibly improves academic success. Exploratory (Performance → Motivation) : A significant feedback from academic outcomes to motivation (β = 0.27, SE = 0.06, t = 4.68, p < .001) was also found. In other words, students with better academic performance became more motivated. This reciprocal effect, though smaller than the forward effect, suggests that success breeds motivation. A student performing well likely gains confidence and enthusiasm to continue exerting effort. The mutual influence between motivation and performance can create a positive upward spiral: initial motivation boosts performance, and successful performance then reinforces motivation. The inclusion of this AO → AM path modestly but significantly improved model fit (Δχ²(1) = 7.59, p = .006) compared to a model without it, indicating it is a meaningful dynamic to consider. It should be noted that cross-sectional data cannot confirm temporal causality; however, this pattern is theoretically plausible and supported by academic self-concept research (Marsh & Craven, 2006) showing achievement feedback into motivation/self-concept. H3 (Motivation and Performance → Well-Being) : Turning to well-being, academic motivation had a positive direct effect on well-being (β = 0.38, SE = 0.07, t = 5.68, p < .001). Thus, students who were more autonomously motivated reported higher psychological well-being. This finding is consistent with SDT’s assertion that autonomous motivation contributes to well-being because it satisfies innate needs and aligns with one’s values (Ryan & Deci, 2000). It also echoes empirical studies linking academic motivation to outcomes like life satisfaction and lower burnout (Tang et al., 2021; Baker, 2004). Additionally, academic performance exhibited a significant direct effect on well-being (β = 0.40, SE = 0.06, t = 6.36, p < .001). Students with stronger academic outcomes (higher grades and satisfaction) tended to feel more fulfilled and happy. Achieving academic goals likely boosts well-being via enhanced self-esteem and reduced academic stress. The model suggests that doing well in school contributes about equally to student well-being as being motivated does, with both factors having moderate effects (~ .38–.40). Together, motivation and performance accounted for one-third of the variance in well-being, even after controlling for other factors. Notably, the researcher did not include a direct path from perceived employability to well-being in the final model because it was not statistically significant when motivation and performance were in the model (the direct coefficient was small, β ≈ 0.08, p = .14). This implies that any influence of employability on well-being might be largely indirect – for instance, through motivation (PE → AM → WB) or through reducing stress. This was further examined through mediation analysis, as detailed in the following section. H? (Need Satisfaction → Other Outcomes) : Although not a primary hypothesis, it was examined whether basic need satisfaction had any direct residual effects on academic performance or well-being beyond its impact through motivation. In the tested model, direct paths from BPN to AO and from BPN to WB were initially included but were not significant ( p > .10 for both) when motivation was present as a mediator. This is theoretically sensible: BPN’s influence on performance and well-being may be fully mediated by motivation (and related processes). A model allowing BPN → WB direct did not improve fit (Δχ² was negligible and non-significant). Thus, a parsimonious model was retained in which the effects of Basic Psychological Needs (BPN) on key outcomes were found to operate indirectly through academic motivation and its subsequent pathways, rather than through direct effects. This mediation was supported by indirect effect analysis: the indirect effect of BPN on well-being via AM (and AO) was positive and significant (β_indirect ≈ 0.23, 95% CI [0.15, 0.33], p < .001), whereas the direct BPN → WB effect was near zero. Similarly, the indirect effect of BPN on academic performance via AM was significant (β_indirect ≈ 0.34, 95% CI [0.22, 0.47], p < .001). These results are in line with prior studies suggesting that the relationship between need satisfaction and academic performance is primarily mediated by motivational and affective factors (González et al., 2020; Gillet et al., 2019). Table 2 Standardized Path Coefficients in Structural Model Predictor → Outcome β (Standardized) SE t p Basic Need Satisfaction → Academic Motivation (AM) 0.61*** 0.05 12.04 < .001 Basic Need Satisfaction → Perceived Employability (PE) 0.49*** 0.06 8.78 < .001 Academic Motivation (AM) → Perceived Employability (PE) 0.42*** 0.06 7.39 < .001 Perceived Employability (PE) → Academic Motivation (AM) 0.31*** 0.07 5.01 < .001 Academic Motivation (AM) → Academic Outcomes (AO) 0.55*** 0.08 8.04 < .001 Academic Outcomes (AO) → Academic Motivation (AM) 0.27*** 0.06 4.68 < .001 Academic Motivation (AM) → Well-Being (WB) 0.38*** 0.07 5.68 < .001 Academic Outcomes (AO) → Well-Being (WB) 0.40*** 0.06 6.36 < .001 Model fit: χ²(5) = 12.45, p = .029; CFI = 0.988; TLI = 0.962; RMSEA = 0.045; SRMR = 0.021. Unstandardized coefficients, standard errors (SE), and test statistics are from maximum likelihood estimation. All coefficients shown are standardized (completely standardized solution). *** p < .001. For brevity, paths from control variables (gender, age) are not shown; none of the controls had a significant effect except that female gender predicted slightly higher well-being (β = 0.10, p = .021). In addition to these primary results, specific indirect pathways were examined to further elucidate the mechanism: BPN → AM → AO (mediation) : The indirect effect of basic need satisfaction on academic performance through academic motivation was significant (indirect β = 0.61 * 0.55 = 0.34, p < .001). This indicates that satisfying students’ basic needs contributes to better grades largely by enhancing their motivation to study. Need satisfaction alone did not directly improve GPA once motivation was accounted for, underscoring the mediating role of motivation. Educationally, this means interventions to support student needs (e.g., autonomy-supportive teaching, competence feedback, fostering community) will likely pay off in performance only if they successfully increase students’ intrinsic motivation and engagement. AM → AO → WB (mediation) : Academic motivation also had an indirect effect on well-being via academic outcomes (indirect β = 0.55 * 0.40 = 0.22, p < .01). Part of the reason motivated students are happier is because they achieve more, which in turn boosts well-being. However, even controlling for performance, motivation retained a direct effect on well-being (β = 0.38), suggesting motivation influences well-being through other channels as well – likely through daily enjoyment of learning and a sense of purpose. BPN → AM → WB : As noted earlier, the effect of need satisfaction on well-being was fully mediated by academic motivation (and further by performance). The chain BPN → AM → WB was significant (β ≈ 0.23, p < .001). Additionally, BPN satisfaction had an alternative path to WB through both AM and AO: BPN → AM → AO → WB (β ≈ 0.61 0.55 0.40 = 0.13, p < .01). Summing these, basic needs indirectly influence well-being considerably. This aligns with SDT’s claim that need fulfillment enhances well-being, and the model clarifies that for students, this occurs by first fostering motivation and success experiences. PE and WB : Although the direct PE → WB path was not significant, the indirect effect of perceived employability on well-being was tested through academic motivation. That indirect path (PE → AM → WB) was significant (β_indirect = 0.31*0.38 = 0.12, 95% CI [0.06, 0.19]). This implies that one way students’ employability outlook affects their well-being is by influencing how motivated and invested they are in their academic activities. A student confident in getting a job likely engages more in studies (as found with PE → AM), which then contributes to feeling more fulfilled. It is also noted that perceived employability may reduce career-related anxieties (not directly measured here), which could positively affect well-being – a pathway supported in other research (Petruzziello et al., 2022). Post-hoc Analysis: Group Differences Exploratory multi-group SEM analyses were conducted to see if the model differed by gender or academic discipline. Multi-group comparison by gender (male vs. female) revealed no significant differences in path coefficients; a model with all paths constrained equal fit well (Δχ² was not significant), suggesting the structural relationships hold similarly for male and female students. Both groups showed the same pattern of significant effects. The sample was split roughly into STEM majors vs. non-STEM majors; again, the model was largely invariant, though it was observed that the PE ↔ AM reciprocal link was slightly stronger among non-STEM students (perhaps because employability in non-technical fields might be perceived as more contingent on personal effort and networking). However, these differences were not statistically significant at the .05 level. Finally, year of study did not meaningfully alter the model either – the relations were consistent from first-year through senior students, though mean levels of PE did increase with year (as expected, nearing graduation). These consistency checks bolster the robustness of the findings across subpopulations. Figure 2 illustrates the validated SEM model with all significant standardized path coefficients. The figure highlights the reciprocal nature of the relationships among academic motivation, perceived employability, and academic outcomes, as well as the downstream influence on well-being. Basic need satisfaction emerges as an exogenous driver with wide-ranging indirect effects. The model indicates that by nurturing their basic psychological needs, students become more motivated; this motivation not only improves their academic success but also enhances their future career confidence and personal well-being. Discussion This study set out to integrate key motivational and outcome variables into a single explanatory model for university student development. Using a large sample of Greek undergraduates,it was examined how the fulfillment of basic psychological needs relates to academic motivation, and in turn how motivation connects to perceived employability, academic performance, and psychological well-being. Overall, the findings provide strong support for the proposed model and offer novel insights into the interplay between academic experiences and career outlooks in higher education. In this section, the implications of each main finding in light of existing literature are discussed, addressing potential limitations, and suggesting directions for future research and practice. The Crucial Role of Basic Needs in Academic Motivation One of the clearest findings was that satisfaction of autonomy, competence, and relatedness needs is a powerful predictor of academic motivation (β = 0.61). This aligns perfectly with Self-Determination Theory (Deci & Ryan, 2000), reinforcing that when students feel their basic needs are met in the university context, they internalize more autonomous motivation for learning. For example, a student who feels autonomously supported by instructors, competent in mastering coursework, and socially connected with peers is far more likely to find inherent value and enjoyment in their studies. The result is consistent with prior studies in Western and non-Western settings (e.g., Del Valle et al., 2025; Liu et al., 2024) that show need satisfaction correlates with higher quality motivation and engagement. It extends those results by demonstrating this effect in Greek higher education—a context that has its own cultural and institutional characteristics. Notably, Greek universities have traditionally large class sizes and lecture-based teaching, which could challenge autonomy and relatedness need fulfillment. Yet, the data suggest that even within this context, variations in perceived need support (perhaps through caring faculty or collaborative learning opportunities) significantly impact student motivation. Practically, this underscores the importance for educators and institutions to cultivate a need-supportive academic environment. Interventions might include training faculty in autonomy-supportive teaching methods (e.g., providing choice in assignments, acknowledging student perspectives), establishing competence-building feedback systems, and fostering a sense of community on campus. By doing so, universities can indirectly boost academic motivation with downstream benefits for performance and well-being. This study thereby adds to the evidence base advocating for student-centered learning climates in higher education (Jeno et al., 2018; Orsini et al., 2015). Interestingly, it was found that basic need satisfaction did not have significant direct effects on academic performance or well-being once motivation was accounted for. Instead, its influence was fully channeled through academic motivation (and subsequent variables). This suggests that need satisfaction by itself may not raise grades or happiness unless it translates into greater self-determined motivation. In other words, it is the energized, purposeful behavior resulting from need satisfaction that yields tangible outcomes. This fully mediated pattern is consistent with SDT’s process view (Ryan & Deci, 2017), and empirically concurs with recent work by González-Arias et al. (2025), who found BPN satisfaction indirectly affected grades via positive affect and motivation. It highlights a mechanism: need-supportive environments → autonomous motivation → better effort and coping → higher achievement and well-being. For researchers, it reinforces treating motivation as a mediator in models of need effects; for practitioners, it means that simply giving resources or support is not enough—students must internalize it (as motivation) for benefits to manifest. Academic Motivation and the Feedback Loop with Performance and Employability Consistent with a wealth of literature, academic motivation strongly predicted academic performance (β = 0.55). This finding echoes the sentiment that “motivation matters” for student success (Richardson et al., 2012; Robbins et al., 2004). Motivated students likely invest more time studying, use deeper learning strategies, and persist through difficulties (Pintrich, 1999; Kusurkar et al., 2013), leading to better academic results. The study contributes additional evidence in a Greek context, suggesting that initiatives to boost students’ motivation (especially intrinsic motives for learning) could be an effective lever to improve academic outcomes in Greek universities. This is particularly relevant given concerns about prolonged study durations and high drop-out rates in some Greek higher education programs (OECD, 2020). By targeting motivation, educators can indirectly influence performance metrics. For instance, mentoring programs, goal-setting workshops, and value-reappraisal exercises have been shown to enhance student motivation and subsequently academic achievement (Harackiewicz et al., 2016; Schutte & Malouff, 2019). Beyond the expected motivation → performance link, the model revealed a reciprocal performance → motivation effect (β = 0.27). This two-way relationship aligns with the notion of a self-reinforcing cycle between achievement and motivation known in educational psychology (Marsh & Martin, 2011). Success in academics can boost one’s academic self-concept and self-efficacy, which then fuels further motivation (Bandura, 1997; Komarraju & Nadler, 2013). For example, getting a high grade on an exam may make a student more confident and interested in the subject, spurring them to engage even more deeply in future learning. While the cross-sectional data cannot confirm temporal causality, the significant bidirectional paths in SEM align with longitudinal research showing reciprocal effects between academic self-beliefs/motivation and achievement over time (Guay et al., 2003; Corpus et al., 2020). This finding suggests that interventions can potentially initiate positive spirals. Early academic successes (even small ones) could be leveraged to increase motivation which then leads to larger successes. Educational programs might focus on providing early feedback and mastery experiences for first-year students to kick-start this virtuous cycle. One of the novel contributions of this study is integrating perceived employability into the academic motivation-performance dynamic. Clear evidence that academically motivated students tend to feel more employable (β = 0.42) is found. This supports the idea that engaged students build human capital and networks that bolster their confidence in securing a job (Clarke, 2018; Tentama & Arridha, 2020). A motivated student likely participates in internships, takes leadership roles in projects, and attains skill certificates – all of which enhance employability. Moreover, being motivated may simply make students more optimistic and proactive about career planning, thus raising their perceived employability (which often involves an element of optimism about job prospects). Crucially, the reverse path is also found: students who believed strongly in their employability had higher academic motivation (β = 0.31). To the knowledge, this reciprocal relation has seldom been empirically demonstrated, especially in a single-time SEM. It resonates with findings by Bozgeyikli et al. (2023) who reported that students with greater confidence in their career opportunities showed higher academic effort and persistence, presumably because they see a clearer payoff to academic work. Theoretically, this can be framed in terms of expectancy-value models (Eccles & Wigfield, 2002): if a student expects that doing well in their studies will lead to a good job (high outcome expectancy), their task motivation should increase. High perceived employability might thus strengthen the perceived instrumentality of academic tasks for achieving career goals, thereby motivating students to invest in those tasks (Karimi & Sotoodeh, 2019). The interplay between motivation and employability has implications for how universities approach career services and academic advising. The results suggest that academic affairs and career services should not operate in isolation. Enhancing students’ employability (through career counseling, resume workshops, employer networking events, etc.) might have a positive feedback on their academic engagement. When students feel their studies are leading somewhere tangible, they may approach coursework with greater vigor. Conversely, boosting students’ academic motivation (through pedagogical improvements or mentoring) can improve not just grades but also students’ outlook on their career readiness. This calls for an integrated strategy where academic support and career development are linked. For example, incorporating career-relevant projects into the curriculum or highlighting transferable skills gained in coursework could simultaneously satisfy academic requirements and increase students’ sense of employability. The findings add empirical weight to calls for bridging academic learning and employability in higher education (Tomlinson, 2017; Artess et al., 2017). It is important to note that while a strong connection between perceived employability and academic motivation is found, the relationship between perceived employability and actual academic performance was weaker (the correlation was r ≈ .22, and no direct structural path was posited from PE to AO). This suggests that simply feeling employable does not automatically translate to better grades – it influences performance largely via motivation. A student might be confident about getting a job through other experiences (social capital, etc.) and not necessarily have top grades. This nuance aligns with studies which find that employability perceptions correlate more with soft outcomes like self-efficacy and networking behaviors than with GPA (Rothwell et al., 2008). It reinforces that perceived employability is a distinct construct – related to but not identical with academic success. Academic Outcomes and Student Well-Being The model highlights academic success and motivation as significant contributors to students’ well-being. Academic performance had a moderate positive effect on psychological well-being (β = 0.40), even when controlling for other factors. This finding contributes to a growing body of evidence that doing well academically can enhance students’ mental health and life satisfaction. One explanation is that academic success fulfills competence needs and provides a sense of accomplishment, which is inherently gratifying (Sheldon et al., 2019). Additionally, good performance may reduce academic stress (e.g., less worry about failing or retaking courses), thereby improving overall well-being. The result dovetails with Trucchia et al. (2013) who found higher well-being among high-performing students, and with survey research showing positive associations between college GPA and indicators of well-being and adjustment (Salanova et al., 2010). It is also consistent with the idea of “academic buoyancy” – students who achieve academic goals have greater resilience and positive affect to handle other life challenges (Martin & Marsh, 2008). For Greek students, given the societal value placed on academic success and limited job opportunities, performing well can be a significant relief and source of pride, potentially explaining the boost to well-being. Academic motivation also independently predicted well-being (β = 0.38). This underscores that being autonomously motivated – finding joy and value in one’s studies – is beneficial for mental health in its own right. A student who is studying out of genuine interest and personal endorsement likely experiences less internal conflict, more positive emotions, and a greater sense of purpose (Niemiec & Ryan, 2009). These factors contribute to subjective well-being. The results align with SDT research showing that autonomous motivation correlates with higher life satisfaction and lower burnout among students (Baker, 2004; Jungert et al., 2018). It extends this knowledge by quantifying the effect in a multivariate context; even considering academic outcomes, motivation had a unique positive link to well-being. This suggests that how students approach learning (their motivation) can matter as much as how much they achieve in determining their happiness. In practical terms, educational policies that emphasize student well-being should not only focus on academic support for higher grades but also on fostering a climate where learning is enjoyable and meaningful. Strategies like project-based learning, autonomy in course selection, and connecting coursework to students’ personal goals could maintain or increase intrinsic motivation, which in turn keeps students psychologically healthier. The findings did not confirm a direct effect of perceived employability on well-being once other factors were controlled, yet the positive correlation (r = .34) and indirect effect via motivation suggest something important: believing one has good job prospects is associated with feeling better. It may be that perceived employability’s influence on well-being is partly captured by the fact that it spurs students to be more engaged (hence happier through motivation) and partly by unmeasured factors like reduced financial anxiety. The literature notes that perceived employability can buffer the impact of stressors on well-being (De Cuyper et al., 2012; Rothwell et al., 2015); for students, high perceived employability might mitigate the distress that comes with uncertainty about the future, thereby indirectly sustaining higher well-being. The sample being in pre-graduation years might not yet fully experience the stress of job searching, which could be why the direct effect was small here. Future longitudinal research following students into post-graduation employment could clarify how perceived employability during university relates to later mental health outcomes. Limitations and Future Directions While this study has multiple strengths – including a large sample, use of validated measures, and a comprehensive SEM analysis – certain limitations warrant caution and point to avenues for further research. First, the cross-sectional design limits causal inferences. Although the structural model was theoretically grounded and alternative models were tested, the analysis does not allow for a definitive determination of temporal precedence among the variables. The reciprocal effects detected (AM with PE and AO) should be interpreted carefully; longitudinal or cross-lagged panel studies are needed to verify these two-way influences over time. Future research could track students across several semesters to see if increases in motivation lead to later increases in perceived employability and vice versa. An ideal design would be a three-wave longitudinal study, measuring these constructs annually from freshman to senior year, which would allow cross-lagged SEM to disentangle directionality. Second, all data were based on self-reports, which introduces the potential for common method variance and self-report biases. The researcher attempted to mitigate this (anonymous survey, mixing item order, using established scales), and a Harman’s single-factor test did not indicate a general factor issue. Nonetheless, the relationships (especially between motivation, employability, and well-being) might be inflated by positive response tendencies or personality traits (optimism, etc.). Including external measures such as actual academic records (for GPA) or observer ratings could strengthen future studies. For example, it would be informative to incorporate instructor-rated motivation or an objective test of academic skills to complement self-reported motivation and outcomes. Additionally, perceived employability could be complemented with more objective indicators of employability, such as number of internships or proficiency in job-market skills, to see how those align with student perceptions and outcomes. Third, the measure of academic outcomes was primarily GPA and satisfaction. GPA in different fields and universities might not be strictly comparable due to grading norms. The scores were standardized and also included satisfaction to partly counteract that. Still, future studies could examine other academic success metrics like credits earned, on-time graduation, or achievement relative to peers. Moreover, academic outcomes can be broadened beyond performance to include learning outcomes or skill acquisition, which were not directly measured. Similarly, well-being was measured as flourishing (positive functioning); inclusion of measures for negative outcomes (like anxiety or depression scales) could provide a fuller picture of student mental health. It would be valuable to know if motivation and need satisfaction protect against distress in addition to promoting positive well-being. Fourth, while the sample was diverse in majors, it was exclusively Greek students. Cultural and educational system factors may affect generalizability. In Greece, public universities are tuition-free and there is less continuous assessment during the semester compared to US or UK systems; this might influence student motivation dynamics. Also, the Greek job market has unique challenges that shape students’ views of employability. Thus, replication in other countries is encouraged. It is anticipated that the core model (need satisfaction → motivation → outcomes → well-being, with motivation ↔ employability) would hold in many contexts, but the magnitude of effects might vary. For instance, in countries with lower youth unemployment, the link between motivation and employability perceptions might be weaker because students take employability for granted. Cross-cultural comparisons could be enlightening. Additionally, the investigation of perceived employability was cross-sectional; an interesting future direction is to see how university experiences affect employability perceptions and actual employment outcomes after graduation. A longitudinal design could test if academic motivation during college predicts not only perceived but actual employability (e.g., number of job offers, speed to employment) post-graduation, controlling for academic performance. This would bridge the gap between students’ subjective outlooks and objective career outcomes. Finally, although the model was comprehensive, there are other relevant constructs not included that could enrich understanding. For example, academic self-efficacy is closely tied to both motivation and performance (Zimmerman, 2000), and career adaptability or career planning behaviors could also play a role in employability and well-being (Savickas & Porfeli, 2012). Incorporating these could form an even more nuanced model. The researcher also did not explicitly model extrinsic vs. intrinsic motivation subtypes due to complexity, but doing so could reveal if intrinsic motivation is the main driver of the positive effects (as SDT would predict) while controlled motivations have different or negative relations. Similarly, need frustration (the opposite of need satisfaction) can predict ill-being (Bartholomew et al., 2011); an intriguing extension would be to examine need frustration among students and whether it leads to demotivation, poorer performance, and distress. Implications for Higher Education Practice Despite these limitations, the present findings carry several important implications for higher education policy and practice, particularly in the Greek context but also more broadly. First, the strong influence of basic psychological need satisfaction calls for universities to evaluate and improve how they support students’ autonomy, competence, and relatedness. Faculty development programs can emphasize autonomy-supportive teaching (e.g., giving students choice in projects, encouraging self-initiation). Academic support services can bolster competence by helping students set optimal challenges and providing positive feedback. Student affairs can cultivate relatedness by promoting mentoring programs, study groups, and inclusive campus communities. The payoff for such efforts is likely multifaceted: as the model shows, need satisfaction can cascade into better motivation, performance, and student well-being. In an era where student mental health is a growing concern in universities worldwide, addressing environmental supports for basic needs might be a preventative approach to foster resilience and enthusiasm. Second, the finding that academic motivation and success enhance perceived employability suggests that academic and career advising should be interconnected. Universities might implement programs that explicitly link academic engagement to career development. For example, incorporating real-world projects or internships into curricula can simultaneously stimulate intrinsic motivation (by providing meaningful learning contexts) and improve students’ job readiness. Career centers can collaborate with academic departments to identify academically disengaged students and provide career mentoring to help them see the relevance of their studies to future goals – potentially reigniting their motivation. Additionally, providing students with feedback on how their academic progress contributes to skill sets valued by employers could boost their self-perceived employability. In Greece, where students may feel uncertain about job prospects, highlighting the connection between academic competencies and employment (perhaps via alumni testimonials or industry panels) could both motivate current study and alleviate future anxiety. Third, the results concerning well-being underline that academic policies should not solely prioritize grades but also students’ psychological welfare. Over-emphasis on performance without regard to motivation quality may backfire; a student could achieve high grades under pressure yet suffer burnout or poor well-being. Instead, focusing on cultivating intrinsic motivation might yield high performance alongside better well-being – a win-win. Universities might thus consider incorporating well-being and motivation indicators into their institutional assessments of educational quality. For instance, student surveys could track not just satisfaction with courses but also how motivated and supported students feel, using those data to improve programs. The positive relationship between performance and well-being also suggests that helping students succeed academically (through tutoring, early interventions for struggling students, etc.) is likely beneficial for their mental health. Lastly, the study has relevance for educators in framing the narrative of higher education to students. By demonstrating empirically that academic motivation and success contribute to feeling prepared for the job market and to personal happiness, an encouraging message is provided: engaging deeply in one’s education is not only about grades – it has broader payoffs for one’s future and quality of life. Communicating this to students could potentially create a more internally driven student body. In settings like Greece, where external challenges (economic difficulties) might dampen student morale, emphasizing internal growth and linking it to future opportunities can be empowering. Conclusion In conclusion, the present research offers an integrative understanding of how psychological, academic, and career-related factors jointly influence university students’ outcomes. The structural model – supported by data from Greek higher education – underscores that fulfilling students’ basic psychological needs is the foundation for robust academic motivation, which in turn drives both superior academic performance and a confident outlook on employability. These academic gains and career confidence are not attained at the expense of personal well-being, but rather coincide with enhanced well-being. In fact, academic motivation and success emerge as significant contributors to students’ psychological flourishing. The complex interrelations found (including reciprocal effects between motivation, performance, and employability perceptions) highlight that a student’s academic journey and career trajectory are deeply intertwined aspects of their overall development. By bridging academic motivation theory (SDT) with career development constructs in a single model, this study contributes to a more holistic view of student development in higher education. The findings encourage educators and policymakers to adopt a dual-focus approach: promoting academic excellence hand-in-hand with nurturing the internal motivational and emotional conditions that make such excellence sustainable and meaningful. As higher education faces evolving challenges – from student mental health crises to demands for graduate employability – the results advocate for strategies that do not treat these issues in isolation. Instead, the key may lie in creating enriching educational environments that simultaneously inspire students to learn, equip them with competencies for the future, and support their well-being. Students who are excited about learning and feel supported are likely to perform better, foresee a brighter career, and thrive personally. In essence, the path to producing successful and well-rounded graduates may begin with something as simple, yet profound, as ensuring students truly want to learn and feel good about doing so. Declarations Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution L.M. wrote the main manuscript, conducted the statistical analysis. The whole manuscript was conducted and prepared by L.M. Data Availability Data cannot be shared openly but may be available on request from authors. References Artemis, K., & Kounenou, K. (2020). Academic motivation and career decision-making among Greek university students . Hellenic Journal of Psychology, 17 (3), 255–271. Aydın, U., & Michou, A. (2020). Need satisfaction and need frustration as distinct contributors to academic motivation: Their interplay with person-oriented and task-oriented perfectionism. Learning and Individual Differences, 78 , 101821. Baker, S. R. (2004). Intrinsic, extrinsic, and amotivational orientations: Their role in university adjustment, stress, well-being, and subsequent academic performance. 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The transition to university life brings new challenges that can test students\u0026rsquo; engagement and well-being (Leow et al., 2023; Brahm et al., 2017). Motivated students tend to show greater academic engagement, higher self-efficacy, and better well-being. Conversely, demotivation can lead to disengagement and even dropout (Brahm et al., 2017). Given these stakes, understanding the factors that cultivate academic motivation and how motivation translates into tangible outcomes is of paramount importance for educators and psychologists alike.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBasic Psychological Needs and Academic Motivation\u003c/strong\u003e \u003cp\u003eSelf-Determination Theory (SDT) provides a robust framework for examining student motivation in educational context. SDT posits that all individuals have three fundamental psychological needs \u0026ndash; autonomy, competence, and relatedness \u0026ndash; which must be satisfied to foster optimal motivation and well-being (Deci \u0026amp; Ryan, 2000; Ryan \u0026amp; Deci, 2017). Autonomy refers to feeling volitional and having a sense of choice in one\u0026rsquo;s actions; competence involves feeling effective and capable of meeting challenges; relatedness entails feeling connected and supported by others.. When the academic environment satisfies these basic psychological needs, students are more likely to develop autonomous forms of academic motivation (e.g. intrinsic motivation) and experience positive outcomes. Prior research consistently shows that need satisfaction is associated with higher quality motivation and positive affect in students (Black \u0026amp; Deci, 2000; Schutte \u0026amp; Malouff, 2021). For example, fulfillment of autonomy and competence needs has been linked to greater intrinsic motivation for learning. In turn, autonomous academic motivation \u0026ndash; the drive to learn out of genuine interest or personal value \u0026ndash; is a strong predictor of student engagement and achievement (Ryan \u0026amp; Deci, 2000; Aydın \u0026amp; Michou, 2020). Satisfying basic needs not only energizes motivation but also contributes directly to psychological well-being. Students who feel autonomous, competent, and connected tend to report higher vitality and life satisfaction (Vansteenkiste et al., 2023). In the college context, \u003cb\u003eBasic Psychological Need satisfaction (BPN)\u003c/b\u003e can be facilitated by supportive teaching practices and a positive campus climate, thereby nurturing students\u0026rsquo; internal motivation to learn (Carmona-Halty et al., 2019; Oram \u0026amp; Rogers, 2022; Fierro-Suero et al., 2022).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAcademic Motivation and Academic Outcomes\u003c/b\u003e: Academic motivation (AM) \u0026ndash; the internal drive or reasons to pursue academic activities \u0026ndash; plays a decisive role in students\u0026rsquo; academic success (Richardson et al., 2012). According to SDT, motivation quality lies on a continuum from controlled (extrinsic) to autonomous (intrinsic) motivation (Ryan \u0026amp; Deci, 2000). Autonomous motivation, especially intrinsic motivation (engaging in learning for its own sake and enjoyment), is linked to deep learning strategies, persistence, and better performance (Pintrich, 1999; Steinmayr \u0026amp; Spinath, 2009). Empirical studies affirm that more motivated students achieve higher grades and academic accomplishments. A meta-analysis by Richardson et al. (2012) found that motivational factors significantly correlate with college GPA. Longitudinal research likewise indicates that students\u0026rsquo; motivation (e.g., need for achievement, intrinsic goals) can predict academic performance even beyond prior achievement and cognitive ability (Steinmayr \u0026amp; Spinath, 2009). In the present study, \u003cb\u003eAcademic Outcomes (AO)\u003c/b\u003e refer to students\u0026rsquo; academic performance, operationalized as self-reported grade point average and related academic achievement indicators. Academic motivation is expected to serve as a key driver of academic outcomes: students with greater motivation to learn are hypothesized to attain better academic performance (Hypothesis 1). This is consistent with abundant evidence that motivation to learn promotes the effort and effective study strategies that lead to higher achievement. Notably, motivation and performance may also reinforce each other reciprocally: experiencing academic success can strengthen a student\u0026rsquo;s motivation further, creating a positive feedback loop (Bandura, 1993; Martin, 2009). Students who perform well often gain confidence and find studies more rewarding, which can enhance future intrinsic motivation. The model explores not only the influence of academic motivation on subsequent performance but also the potential reverse influence of prior performance on current motivation, as part of an exploratory analysis of feedback effects.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePerceived Employability in Higher Education\u003c/b\u003e: In addition to academic goals, today\u0026rsquo;s university students are keenly aware of their future employability \u0026ndash; i.e. their prospects of securing meaningful employment after graduation. \u003cem\u003ePerceived employability (PE)\u003c/em\u003e is defined as a student\u0026rsquo;s perception of his or her ability to obtain and maintain a job appropriate to one\u0026rsquo;s field of study (Rothwell et al., 2008). It reflects the individual\u0026rsquo;s confidence in having the skills, experiences, and attributes that make one attractive to employers (Fugate \u0026amp; Kinicki, 2008; Rothwell \u0026amp; Arnold, 2007). For example, a student with high perceived employability believes that he/she can successfully find a quality job upon graduating. Perceived employability is a growing concern in higher education due to competitive labor markets and high youth unemployment in some regions (Tomlinson, 2007). In Greece, youth unemployment has remained among the highest in Europe \u0026ndash; over 30% in recent years (World Bank, 2023) \u0026ndash; making employability a salient issue for Greek students approaching the workforce. Research suggests that academic experiences can shape students\u0026rsquo; sense of employability. Students who are more engaged and successful academically tend to develop greater self-efficacy and transferable skills, which can bolster their perceived employability (Clarke, 2018). Indeed, \u003cb\u003eacademic motivation\u003c/b\u003e may play a critical role: motivated students likely pursue internships, networking, and skill-building opportunities that enhance their employability, and they may feel more confident in their career prospects as a result. Recent studies have started to link academic motivation with perceived employability. For instance, a survey by Tentama and Arridha (2020) demonstrated a strong positive correlation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.75) between learning motivation and perceived employability among students, with motivation accounting for over 55% of the variability in employability. Similarly, Bozgeyikli et al. (2023) found that higher academic motivation was associated with significantly greater perceived employability in undergraduates, even after controlling for gender and socioeconomic background. These findings align with social-cognitive theories of career development, which propose that positive outcome expectations (e.g., expecting to find a good job) can reinforce one\u0026rsquo;s motivation to engage in relevant activities (Lent et al., 1994). A bidirectional relationship between academic motivation and perceived employability was hypothesized: not only can motivation enhance a student\u0026rsquo;s confidence in future employability, but students who feel optimistic about their employment prospects may in turn invest more effort in their studies (Hypothesis 2). In other words, perceiving a \u0026ldquo;bright future\u0026rdquo; may energize current academic motivation by providing a clear purpose for one\u0026rsquo;s studies. The model will allow testing of this potential reciprocal influence between \u003cb\u003ePerceived Employability (PE)\u003c/b\u003e and academic motivation in the cross-sectional data.\u003c/p\u003e \u003cp\u003eBeyond its interplay with motivation, perceived employability may also have implications for student well-being. According to Conservation of Resources theory, perceived employability can be viewed as a personal resource that reduces stress about the future (Hobfoll, 2002). Students who believe they are employable likely experience less worry about post-graduation uncertainty, which could translate into better mental health (Chiesa et al., 2018). In working adult populations, empirical evidence shows that higher perceived employability is associated with lower job insecurity and better psychological well-being (De Cuyper \u0026amp; De Witte, 2006; Berntson \u0026amp; Marklund, 2007). Recent research extends this to university students: for example, one study found that perceived employability was positively linked to life satisfaction and flourishing among final-year students (Magnano et al., 2019). Petruzziello et al. (2022) reported that Italian graduates with higher perceived employability experienced fewer COVID-19-related worries and greater psychological well-being during the school-to-work transition. Thus, perceived employability may directly contribute to student well-being (WB) by instilling a sense of security and optimism regarding future career prospects. Within the conceptual framework, well-being is treated as a central outcome alongside academic performance. It is anticipated that both academic motivation and academic outcomes exert significant influence on well-being. Moreover, perceived employability may also have a positive effect on well-being, either directly or indirectly\u0026mdash;for example, through the reduction of academic or career-related stress. Given the limited prior research involving student populations, it is primarily expected that this relationship operates indirectly: motivation and academic performance enhance well-being, with perceived employability functioning as a mediating or parallel variable (Hypothesis 3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eWell-Being and Academic Outcomes\u003c/b\u003e: Finally, the model incorporates students\u0026rsquo; psychological well-being as an ultimate outcome of interest. Well-being is operationalized in terms of subjective well-being and flourishing, capturing students\u0026rsquo; overall mental health, life satisfaction, and sense of thriving. This is an important addition because higher education is not only about academic achievement but also about students\u0026rsquo; personal development and health. Past studies indicate a two-way relationship between academic success and well-being: on one hand, students in better mental health tend to perform better academically (e.g., less anxiety and higher concentration facilitate learning). On the other hand, achieving good academic results can reinforce well-being by boosting self-esteem and satisfaction with one\u0026rsquo;s accomplishments. In support, Trucchia et al. (2013) found that Argentine medical students with higher academic performance reported significantly higher satisfaction and psychological well-being, whereas those with poor grades showed more dissatisfaction and distress. Within the proposed framework, academic motivation is expected to positively influence well-being by promoting internal fulfillment and reducing feelings of alienation in academic pursuits. Additionally, academic success is anticipated to further enhance well-being by fostering a sense of competence and accomplishment. This aligns with SDT\u0026rsquo;s proposition that both need fulfillment and the pursuit of intrinsically valued goals (like meaningful learning) contribute to well-being (Ryan \u0026amp; Deci, 2001; Sheldon \u0026amp; Krieger, 2007).\u003c/p\u003e \u003cp\u003eTo summarize, the present study investigates a comprehensive model (see \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e) linking basic need satisfaction, academic motivation, perceived employability, academic performance, and well-being in a higher education context. This integrated approach bridges educational psychology and career psychology perspectives, examining how the learning environment and motivational processes during university years might ultimately impact not only students\u0026rsquo; grades but also their future outlook and psychological health. The context for the research is Greek higher education, where economic challenges have heightened the relevance of employability and well-being issues among students. By testing this structural model in a large sample of Greek undergraduates, the aim was to address several gaps: (a) the need to integrate academic motivation and career outcome constructs (such as employability) in one theoretical model (cf. Tentama \u0026amp; Arridha, 2020; Bozgeyikli et al., 2023); (b) the lack of empirical data on how basic need satisfaction influences perceived employability in students; and (c) limited evidence on reciprocal effects among motivation, performance, and perceptions of employability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e illustrates the hypothesized conceptual model. It was expected that greater basic psychological need satisfaction would lead to higher academic motivation, which in turn would improve academic performance and well-being. It was anticipated that need satisfaction and motivation would enhance students\u0026rsquo; perceived employability, and that higher perceived employability might feed back to strengthen academic motivation. Additionally, academic performance was expected to positively influence well-being. Formally, the main hypotheses were: (1) BPN satisfaction positively predicts academic motivation; (2) academic motivation positively predicts both academic performance and well-being; (3) academic motivation and BPN satisfaction positively predict perceived employability; (4) perceived employability positively influences academic motivation (reciprocal relationship); and (5) academic performance positively predicts well-being. The model was tested by using structural equation modeling, allowing all specified paths to be estimated simultaneously and evaluating overall model fit against the data.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 701 undergraduate students (57% female) from several universities in Greece participated in this study. Participants were drawn from diverse academic disciplines (e.g., social sciences, engineering, humanities, and business) to enhance generalizability. The sample\u0026rsquo;s mean age was 20.8 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.1, range 18\u0026ndash;29), reflecting a typical traditional student population in Greek higher education. Most participants (\u0026asymp;\u0026thinsp;80%) were in their second to fourth year of study, with the remainder in their first or fifth year. The majority (92%) were full-time students. Participation was voluntary and anonymous. Students were informed about the study\u0026rsquo;s purpose and provided informed consent prior to participation. No compensation was given. The study protocol was reviewed and approved by the institutional ethics committee of the researcher\u0026rsquo; Institution.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eData were collected via an online survey administered mid-semester. Faculty and administrative staff helped disseminate the survey link to students across various departments. Respondents completed the questionnaire in Greek, which took approximately 15\u0026ndash;20 minutes. To encourage honest responses, students were assured that their answers were confidential and would be used only for research purposes. It was also emphasized that there were no right or wrong answers. To minimize common-method bias, some items were reverse-coded, and constructs were presented in mixed order. After survey closure, data were screened for quality; cases with excessive missing data (\u0026gt;\u0026thinsp;50% of items) or invariant responses were excluded (resulting in the final \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;701). The overall response rate was around 35%, which is acceptable for online surveys in this population.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eAll measures were established instruments translated into Greek using standard translation/back-translation procedures. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents descriptive statistics and reliability coefficients for each scale. Unless otherwise noted, participants rated items on a Likert-type scale ranging from 1 (\u0026ldquo;Strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;Strongly agree\u0026rdquo;), with higher scores indicating greater levels of the construct.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBasic Psychological Need Satisfaction (BPN)\u003c/b\u003e: Satisfaction of autonomy, competence, and relatedness needs were assessed using the Basic Psychological Need Satisfaction scale adapted for educational settings (Deci \u0026amp; Ryan, 2000; Chen et al., 2015). The scale contained 9 items (3 per need). Sample items include \u0026ldquo;I feel free to decide how to live my academic life\u0026rdquo; (autonomy), \u0026ldquo;I feel capable of succeeding at my studies\u0026rdquo; (competence), and \u0026ldquo;I feel connected with people in my university\u0026rdquo; (relatedness). Participants indicated how true each statement was for them in the university context. An overall need satisfaction score was computed by averaging all items, with higher scores reflecting greater satisfaction of the three needs. Internal consistency for the composite BPN scale in this study was α\u0026thinsp;=\u0026thinsp;0.88. Prior research supports the validity of combining need satisfaction indicators into an overall index of psychological need fulfillment (Ryan \u0026amp; Deci, 2017; Lombas \u0026amp; Esteban, 2018).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAcademic Motivation (AM)\u003c/b\u003e: Academic motivation was measured with the Academic Motivation Scale (AMS; Vallerand et al., 1992), which was adapted to the Greek language and context. The AMS assesses different types of motivation for attending university (intrinsic motivation, identified regulation, introjected regulation, external regulation, and amotivation). For parsimony and given the focus on overall motivation quality, a composite measure of self-determined academic motivation was used. Specifically, items tapping intrinsic and identified motivation to represent autonomous academic motivation were combined, as these forms are most closely tied to positive outcomes (Ryan \u0026amp; Deci, 2000). The scale included 12 items (e.g., \u0026ldquo;I go to university because I truly enjoy learning new things,\u0026rdquo; \u0026ldquo;I study because education will help me achieve my personal goals\u0026rdquo;). Each item was rated from 1 (\u0026ldquo;Does not correspond at all\u0026rdquo;) to 7 (\u0026ldquo;Corresponds exactly\u0026rdquo;). A mean score was calculated, with higher values indicating stronger autonomous academic motivation. Reliability was excellent (α\u0026thinsp;=\u0026thinsp;0.93). Higher scores on this measure have been linked to deeper engagement and better grades in past studies (Cerasoli et al., 2014; Orsini et al., 2015). Controlled motivation and amotivation items, which were not included in the composite score, were also examined and found to be relatively low within this sample. This pattern suggests that the majority of students demonstrated at least a moderate level of self-determination in their academic pursuits.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003ePerceived Employability (PE)\u003c/b\u003e: Students\u0026rsquo; perceived employability was measured using the scale by Rothwell et al. (2008) tailored for university students. This instrument contains 11 items assessing students\u0026rsquo; confidence in obtaining employment after graduation. It covers internal employability (belief in one\u0026rsquo;s skills, experiences, and network) and external employability (perceived opportunities in the labor market). Example items are: \u0026ldquo;I am confident that I have the skills needed to be successful in the job market\u0026rdquo; and \u0026ldquo;Employers will value the experience and qualifications I gain from my degree.\u0026rdquo; Participants responded on a 5-point scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree). An average PE score was computed. In the sample, Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.90, indicating high reliability. This aligns with previous research that found α\u0026thinsp;\u0026asymp;\u0026thinsp;.88 for the student perceived employability scale (Rothwell et al., 2008). A high score means the student is generally optimistic and confident about securing a good job. It is worth noting that perceived employability is subjective and not an objective measure of job market conditions; however, it strongly correlates with job search activities and career self-efficacy (Rothwell et al., 2008; Pool \u0026amp; Sewell, 2007).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAcademic Outcomes (AO)\u003c/b\u003e: Two self-reported indicators were used to capture academic outcomes: (a) \u003cb\u003eGrade Point Average (GPA)\u003c/b\u003e \u0026ndash; students reported their current cumulative grade average on a 10-point scale (as typically used in Greek universities, where 10\u0026thinsp;=\u0026thinsp;excellent, 5\u0026thinsp;=\u0026thinsp;passing threshold). If a GPA was not available, students estimated their average grade. (b) \u003cb\u003eAcademic Performance Satisfaction\u003c/b\u003e \u0026ndash; students rated how satisfied they were with their academic performance thus far (1\u0026thinsp;=\u0026thinsp;not at all satisfied, 5\u0026thinsp;=\u0026thinsp;very satisfied). These two indicators were standardized and combined to form a latent variable for academic outcomes in the SEM. A latent approach was chosen to account for measurement error and to represent the construct more robustly (Kline, 2016). For descriptive purposes, a composite z-score for academic outcomes was computed. The sample\u0026rsquo;s mean self-reported GPA was 7.45 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.20) on the 0\u0026ndash;10 scale, which corresponds to \u0026ldquo;Good\u0026rdquo; performance. The satisfaction item had a mean of 3.2 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.0) on the 5-point scale. The two indicators were strongly correlated (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.62), supporting their convergence on an underlying performance factor. While GPA is an objective outcome (subject to self-report accuracy), including a satisfaction rating captures the student\u0026rsquo;s subjective appraisal of their academic success. This combination acknowledges that academic achievement is both an external outcome and an internal experience for the student.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWell-Being (WB)\u003c/b\u003e: Students\u0026rsquo; psychological well-being was assessed using the \u003cb\u003eFlourishing Scale\u003c/b\u003e (Diener et al., 2010), which measures general eudaimonic well-being (e.g., sense of purpose, social relationships, self-esteem). It consists of 8 broad statements (e.g., \u0026ldquo;I lead a purposeful and meaningful life,\u0026rdquo; \u0026ldquo;I am optimistic about my future\u0026rdquo;), rated from 1\u0026thinsp;=\u0026thinsp;strongly disagree to 7\u0026thinsp;=\u0026thinsp;strongly agree. This measure was chosen because it provides a holistic gauge of well-being suitable for young adults and has been used in university student samples. The Flourishing Scale yields a single summary score (ranging 8\u0026ndash;56). In this study, a mean well-being score of 45.3 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.1) was obtained, indicating generally high well-being on average. Internal consistency was α\u0026thinsp;=\u0026thinsp;0.87. This was complemented with two items on \u003cb\u003erecent affect\u003c/b\u003e: students rated their general stress level and happiness level in the past month on 5-point scales. These items were used in preliminary analyses to characterize the sample; on average, students reported moderate stress (mean\u0026thinsp;=\u0026thinsp;3.1/5) and relatively high happiness (mean\u0026thinsp;=\u0026thinsp;3.8/5). In the SEM, well-being was modeled as a latent factor indicated by the Flourishing Scale score and (for identification) the life satisfaction item from that scale treated as a separate indicator. This approach recognizes well-being as a multifaceted construct. It should be noted that well-being measure primarily captures positive aspects (flourishing); it does not directly assess mental ill-being (such as anxiety or depression). However, flourishing is strongly negatively associated with such symptoms (Diener et al., 2010), and thus is a suitable positive psychology outcome for this study.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAll scale scores were computed such that higher values reflect more of the construct (higher need satisfaction, motivation, employability, performance, well-being). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the mean, standard deviation, reliability (Cronbach\u0026rsquo;s α), and intercorrelations for all main variables. The correlation matrix revealed significant positive correlations between all pairs of constructs (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, basic need satisfaction was strongly correlated with academic motivation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and well-being (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), consistent with SDT propositions. Academic motivation showed a substantial correlation with perceived employability (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a moderate correlation with GPA (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Perceived employability was moderately correlated with well-being (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that students who felt confident about their job prospects also tended to be more content and psychologically healthy. These correlations set the stage for testing the directional hypotheses via SEM.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics and Correlations among Study Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1. BPN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2. AM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3. PE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4. AO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5. WB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Basic Psychological Need Satisfaction (BPN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Academic Motivation (AM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Perceived Employability (PE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Academic Outcomes (AO)a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.22***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Well-Being (WB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.34***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.31***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;701. M and SD are mean and standard deviation. α\u0026thinsp;=\u0026thinsp;Cronbach\u0026rsquo;s alpha (reliability). Correlations are Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e. ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 (two-tailed)._ a_Academic Outcomes (AO) is a composite z-score of GPA and academic satisfaction._ b_Standard deviation for AO composite (z-score) is approximately 0.96 due to slight deviations from exact standard normal after combining indicators._\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eA two-step modeling approach (Anderson \u0026amp; Gerbing, 1988) was employed using Structural Equation Modeling (SEM). First, a confirmatory factor analysis (CFA) was conducted to verify the measurement model \u0026ndash; i.e., that each set of observed items or indicators loaded appropriately on their intended latent construct (BPN, AM, PE, AO, WB). Given the use of composite scores for some constructs (e.g., BPN, AM, PE had single composite indicators, while AO and WB were indexed by two indicators each), the measurement model in this case was relatively simple. Nonetheless, the two indicators of AO (GPA and performance satisfaction)were allowed to load on the AO factor, and similarly treated Flourishing score and life satisfaction item as indicators of WB. All latent factors were allowed to correlate. The measurement model was evaluated with multiple goodness-of-fit indices: chi-square (χ\u0026sup2;), comparative fit index (CFI), Tucker\u0026ndash;Lewis index (TLI), root mean square error of approximation (RMSEA) with 90% confidence interval, and standardized root mean square residual (SRMR). According to conventional criteria, CFI/TLI values of 0.90 or above indicate acceptable fit (\u0026ge;\u0026thinsp;0.95 for excellent fit), RMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.08 indicates reasonable fit (\u0026le;\u0026thinsp;0.05 excellent), and SRMR\u0026thinsp;\u0026le;\u0026thinsp;0.08 is desirable (Hu \u0026amp; Bentler, 1999; Kline, 2016).\u003c/p\u003e \u003cp\u003eAfter establishing an adequate measurement model, the researcher proceeded to test the \u003cb\u003estructural model\u003c/b\u003e corresponding to the hypotheses (Fig.\u0026nbsp;1\u0026rsquo;s configuration). This model specified directional paths from BPN to AM and PE, from AM to PE, AO, and WB, and from AO to WB. Additionally \u0026ndash; based on theoretical reasoning discussed earlier \u0026ndash;exploratory paths from PE to AM and from AO to AM were included, allowing for potential reciprocal effects. In essence, the model was a partially non-recursive system among AM, PE, and AO (with AM as both predictor and outcome in different relations). To ensure model identifiability in the presence of these reciprocal links, no covariance between the residuals of AM, PE, and AO equations (the reciprocal paths themselves capture their covariance) were imposed. The researcher also controlled for gender and age effects on key endogenous variables by adding paths from these covariates to AM, PE, AO, and WB (though for clarity, these are not depicted in the figures). The structural model was estimated with maximum likelihood (ML) estimation using the \u003cb\u003eAMOS 26\u003c/b\u003e software (IBM SPSS AMOS). Given the sample size (701), ML was appropriate and robust. The researcher checked for any violations of assumptions; Mardia\u0026rsquo;s test indicated some multivariate kurtosis, so ML estimates were supplemented with bootstrapped standard errors and bias-corrected confidence intervals as a robustness check. No significant differences in inferences were found with bootstrapping.\u003c/p\u003e \u003cp\u003eThe overall model fit was evaluated using the same indices mentioned above. The hypothesized model was compared against plausible alternatives. One alternative model tested was a fully mediated model where basic needs influenced outcomes only through motivation (i.e., direct BPN\u0026rarr;AO and BPN\u0026rarr;WB paths were constrained to zero). Another alternative tested the exclusion of the PE\u0026harr;AM reciprocal link (dropping the PE\u0026rarr;AM path). Model comparisons were made via χ\u0026sup2; difference tests (for nested models) and by examining changes in fit indices. Finally, the squared multiple correlations (\u003cem\u003eR\u003c/em\u003e^2^) for each endogenous construct to assess variance explained, was computed.\u003c/p\u003e \u003cp\u003ePath coefficients were considered significant at p\u0026thinsp;\u0026lt;\u0026thinsp;.05, with \"***\" denoting p\u0026thinsp;\u0026lt;\u0026thinsp;.001. Standardized beta (β) weights were used to facilitate interpretation. Indirect effects (mediation) were examined by computing the product of the relevant path coefficients and assessing their significance through the bootstrap method with 5,000 resamples. For example, the indirect effect of basic psychological needs (BPN) on academic outcomes (AO) through academic motivation (AM), as well as the indirect effect of academic motivation on well-being (WB) via academic outcomes, were specifically tested.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted in SPSS and AMOS. Descriptive statistics and bivariate correlations were generated in SPSS, whereas the SEM was run in AMOS. There were very few missing data points (\u0026lt;\u0026thinsp;2% per item); these were handled with full-information maximum likelihood estimation in the SEM, which is appropriate under missing at random assumptions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePreliminary Analysis: Measurement Model and Descriptives\u003c/h2\u003e \u003cp\u003eThe confirmatory factor analysis indicated that the measurement model fit the data well: χ\u0026sup2;(3, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;701)\u0026thinsp;=\u0026thinsp;7.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.050; CFI\u0026thinsp;=\u0026thinsp;0.992; TLI\u0026thinsp;=\u0026thinsp;0.974; RMSEA\u0026thinsp;=\u0026thinsp;0.044 (90% CI [0.000, 0.085]); SRMR\u0026thinsp;=\u0026thinsp;0.017. All observed indicators loaded significantly on their intended latent constructs (all standardized factor loadings\u0026thinsp;\u0026ge;\u0026thinsp;0.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). These results support the distinctness and convergent validity of the constructs. For example, the two indicators of academic outcomes (GPA and performance satisfaction) loaded at 0.80 and 0.77 on the AO factor, and the flourishing scale score loaded 0.88 on the WB factor (with the individual life satisfaction item loading 0.72). The composite scales for BPN, AM, and PE were each treated as single-indicator latent variables; their error variances were set based on scale reliability (e.g., for AM, error variance\u0026thinsp;=\u0026thinsp;variance * (1 \u0026ndash; α)\u0026thinsp;=\u0026thinsp;1.11 * (1\u0026ndash;0.93)). This approach accounts for measurement imperfection in single-indicator constructs (Hayduk, 1987). The high CFI/TLI and low SRMR indicate an excellent measurement structure, giving us confidence to proceed with structural relations.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (see above) shows descriptive statistics and Pearson correlations among all variables. As noted in the Method section, these correlations provided initial support for the theoretical model. Basic need satisfaction was positively correlated with academic motivation (r\u0026thinsp;=\u0026thinsp;.57) and well-being (r\u0026thinsp;=\u0026thinsp;.53). Academic motivation, in turn, correlated moderately with GPA/performance (r\u0026thinsp;\u0026asymp;\u0026thinsp;.30) and with well-being (r\u0026thinsp;=\u0026thinsp;.41). Perceived employability had medium positive correlations with both motivation and well-being. All correlations were significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. These zero-order links suggest that students whose psychological needs are met and who are more intrinsically motivated tend to feel more employable, perform better academically, and have higher well-being. Nevertheless, correlations cannot establish directionality or account for simultaneous influences; hence the need for SEM to test the hypothesized causal paths while controlling for interrelations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStructural Model Results\u003c/h3\u003e\n\u003cp\u003eThe hypothesized structural model (including the reciprocal paths between AM and PE, and between AM and AO) demonstrated a good fit to the data: χ\u0026sup2;(5, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;701)\u0026thinsp;=\u0026thinsp;12.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.029; CFI\u0026thinsp;=\u0026thinsp;0.988; TLI\u0026thinsp;=\u0026thinsp;0.962; RMSEA\u0026thinsp;=\u0026thinsp;0.045 (90% CI [0.013, 0.078]); SRMR\u0026thinsp;=\u0026thinsp;0.021. All fit indices met conventional criteria for acceptable model fit (CFI/TLI close to 0.99, RMSEA\u0026thinsp;~\u0026thinsp;0.05, SRMR\u0026thinsp;~\u0026thinsp;0.02). The model accounted for a substantial proportion of variance in each endogenous construct. Specifically, \u003cb\u003eacademic motivation\u003c/b\u003e (AM) had \u003cem\u003eR\u003c/em\u003e^2^ = 0.59, indicating that about 59% of the variance in AM was explained by its predictors (basic needs, perceived employability, and academic outcomes). \u003cb\u003ePerceived employability\u003c/b\u003e (PE) had \u003cem\u003eR\u003c/em\u003e^2^ = 0.44, \u003cb\u003eacademic outcomes\u003c/b\u003e (AO) had \u003cem\u003eR\u003c/em\u003e^2^ = 0.30, and \u003cb\u003ewell-being\u003c/b\u003e (WB) had \u003cem\u003eR\u003c/em\u003e^2^ = 0.34. These values suggest medium-to-large effect sizes for the model\u0026rsquo;s explanatory power (Cohen, 1988). Figure\u0026nbsp;2 presents the final validated SEM model with standardized path coefficients. All hypothesized paths were positive and statistically significant. The detailed results for each structural path are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These findings in relation to each hypothesis are described:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH1 (BPN \u0026rarr; AM)\u003c/b\u003e: As expected, basic psychological need satisfaction had a strong positive effect on academic motivation (β\u0026thinsp;=\u0026thinsp;0.61, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Students who felt higher autonomy, competence, and relatedness in their university experience were considerably more motivated to study. This standardized coefficient of 0.61 is one of the largest in the model, underscoring the central role of need fulfillment in driving student motivation. This result is consistent with SDT-based studies in education (e.g., Del Valle et al., 2025; Basileo et al., 2024) which have found that satisfaction of basic needs is a proximal predictor of autonomous motivation. The finding extends these results to the Greek university context, reinforcing that when Greek students feel supported and capable in their academic environment, their enthusiasm and internal drive to learn are greatly enhanced.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH2 (Motivation \u0026harr; Employability)\u003c/b\u003e: Academic motivation positively predicted perceived employability (β\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). This indicates that more motivated students tend to feel more confident about their employability. A one-standard deviation increase in academic motivation was associated with a 0.42 SD increase in perceived employability, holding need satisfaction constant. This supports the idea that motivated engagement in one\u0026rsquo;s studies builds skills and self-assurance that translate into perceived career readiness. Importantly, A significant path from perceived employability back to academic motivation (β\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) was also found. Thus, students who believed they are employable were more academically motivated, even after accounting for the effect of need satisfaction. This reciprocal relationship between AM and PE is noteworthy. It suggests a reinforcing cycle: motivation leads students to develop themselves (improving employability), and feeling employable in turn fuels further academic motivation \u0026ndash; perhaps because students see the payoff of education in concrete career terms or have greater optimism that energizes their studies. This bidirectional link is in line with recent findings (Tentama \u0026amp; Arridha, 2020; Bozgeyikli et al., 2023) and provides empirical evidence that academic and career processes are intertwined. However, it is rare for cross-sectional SEM to include non-recursive paths; the researcher ensured the model met identification conditions, and the solution was admissible with a high correlation (r\u0026thinsp;\u0026asymp;\u0026thinsp;.64) between the disturbances of AM and PE equations. The presence of both paths significantly improved model fit compared to a model with only AM \u0026rarr; PE (Δχ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;10.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.0014), indicating that the data favor the inclusion of the feedback effect from PE to AM.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH1 (Motivation \u0026rarr; Performance)\u003c/b\u003e: Academic motivation had a robust positive effect on academic outcomes (β\u0026thinsp;=\u0026thinsp;0.55, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). This confirms that highly motivated students tend to achieve better academically \u0026ndash; they earn higher grades and feel more satisfied with their performance. A standardized coefficient of 0.55 implies that motivation is a powerful predictor: for instance, moving from a low to high motivation (e.g., 2 SD increase) could raise GPA by roughly 1 full grade point in the 10-point system, according to the model. This result aligns with a vast body of literature linking motivation to academic achievement (Richardson et al., 2012; Steinmayr \u0026amp; Spinath, 2009). It highlights that fostering intrinsic academic motivation is not just a feel-good endeavor but tangibly improves academic success.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExploratory (Performance \u0026rarr; Motivation)\u003c/b\u003e: A significant feedback from academic outcomes to motivation (β\u0026thinsp;=\u0026thinsp;0.27, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) was also found. In other words, students with better academic performance became more motivated. This reciprocal effect, though smaller than the forward effect, suggests that success breeds motivation. A student performing well likely gains confidence and enthusiasm to continue exerting effort. The mutual influence between motivation and performance can create a positive upward spiral: initial motivation boosts performance, and successful performance then reinforces motivation. The inclusion of this AO \u0026rarr; AM path modestly but significantly improved model fit (Δχ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;7.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.006) compared to a model without it, indicating it is a meaningful dynamic to consider. It should be noted that cross-sectional data cannot confirm temporal causality; however, this pattern is theoretically plausible and supported by academic self-concept research (Marsh \u0026amp; Craven, 2006) showing achievement feedback into motivation/self-concept.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH3 (Motivation and Performance \u0026rarr; Well-Being)\u003c/b\u003e: Turning to well-being, academic motivation had a positive direct effect on well-being (β\u0026thinsp;=\u0026thinsp;0.38, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Thus, students who were more autonomously motivated reported higher psychological well-being. This finding is consistent with SDT\u0026rsquo;s assertion that autonomous motivation contributes to well-being because it satisfies innate needs and aligns with one\u0026rsquo;s values (Ryan \u0026amp; Deci, 2000). It also echoes empirical studies linking academic motivation to outcomes like life satisfaction and lower burnout (Tang et al., 2021; Baker, 2004). Additionally, academic performance exhibited a significant direct effect on well-being (β\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Students with stronger academic outcomes (higher grades and satisfaction) tended to feel more fulfilled and happy. Achieving academic goals likely boosts well-being via enhanced self-esteem and reduced academic stress. The model suggests that doing well in school contributes about equally to student well-being as being motivated does, with both factors having moderate effects (~\u0026thinsp;.38\u0026ndash;.40). Together, motivation and performance accounted for one-third of the variance in well-being, even after controlling for other factors. Notably, the researcher did not include a direct path from perceived employability to well-being in the final model because it was not statistically significant when motivation and performance were in the model (the direct coefficient was small, β\u0026thinsp;\u0026asymp;\u0026thinsp;0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.14). This implies that any influence of employability on well-being might be largely indirect \u0026ndash; for instance, through motivation (PE \u0026rarr; AM \u0026rarr; WB) or through reducing stress. This was further examined through mediation analysis, as detailed in the following section.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eH? (Need Satisfaction \u0026rarr; Other Outcomes)\u003c/b\u003e: Although not a primary hypothesis, it was examined whether basic need satisfaction had any direct residual effects on academic performance or well-being beyond its impact through motivation. In the tested model, direct paths from BPN to AO and from BPN to WB were initially included but were not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.10 for both) when motivation was present as a mediator. This is theoretically sensible: BPN\u0026rsquo;s influence on performance and well-being may be fully mediated by motivation (and related processes). A model allowing BPN \u0026rarr; WB direct did not improve fit (Δχ\u0026sup2; was negligible and non-significant). Thus, a parsimonious model was retained in which the effects of Basic Psychological Needs (BPN) on key outcomes were found to operate indirectly through academic motivation and its subsequent pathways, rather than through direct effects. This mediation was supported by indirect effect analysis: the indirect effect of BPN on well-being via AM (and AO) was positive and significant (β_indirect\u0026thinsp;\u0026asymp;\u0026thinsp;0.23, 95% CI [0.15, 0.33], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), whereas the direct BPN \u0026rarr; WB effect was near zero. Similarly, the indirect effect of BPN on academic performance via AM was significant (β_indirect\u0026thinsp;\u0026asymp;\u0026thinsp;0.34, 95% CI [0.22, 0.47], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). These results are in line with prior studies suggesting that the relationship between need satisfaction and academic performance is primarily mediated by motivational and affective factors (Gonz\u0026aacute;lez et al., 2020; Gillet et al., 2019).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized Path Coefficients in Structural Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor \u0026rarr; Outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (Standardized)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic Need Satisfaction \u0026rarr; Academic Motivation (AM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.61***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic Need Satisfaction \u0026rarr; Perceived Employability (PE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Motivation (AM) \u0026rarr; Perceived Employability (PE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Employability (PE) \u0026rarr; Academic Motivation (AM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Motivation (AM) \u0026rarr; Academic Outcomes (AO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Outcomes (AO) \u0026rarr; Academic Motivation (AM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Motivation (AM) \u0026rarr; Well-Being (WB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Outcomes (AO) \u0026rarr; Well-Being (WB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel fit: χ\u0026sup2;(5)\u0026thinsp;=\u0026thinsp;12.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.029; CFI\u0026thinsp;=\u0026thinsp;0.988; TLI\u0026thinsp;=\u0026thinsp;0.962; RMSEA\u0026thinsp;=\u0026thinsp;0.045; SRMR\u0026thinsp;=\u0026thinsp;0.021. Unstandardized coefficients, standard errors (SE), and test statistics are from maximum likelihood estimation. All coefficients shown are standardized (completely standardized solution). ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. For brevity, paths from control variables (gender, age) are not shown; none of the controls had a significant effect except that female gender predicted slightly higher well-being (β\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.021).\u003c/p\u003e \u003cp\u003eIn addition to these primary results, specific indirect pathways were examined to further elucidate the mechanism:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBPN \u0026rarr; AM \u0026rarr; AO (mediation)\u003c/b\u003e: The indirect effect of basic need satisfaction on academic performance through academic motivation was significant (indirect β\u0026thinsp;=\u0026thinsp;0.61 * 0.55\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). This indicates that satisfying students\u0026rsquo; basic needs contributes to better grades largely by enhancing their motivation to study. Need satisfaction alone did not directly improve GPA once motivation was accounted for, underscoring the mediating role of motivation. Educationally, this means interventions to support student needs (e.g., autonomy-supportive teaching, competence feedback, fostering community) will likely pay off in performance only if they successfully increase students\u0026rsquo; intrinsic motivation and engagement.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAM \u0026rarr; AO \u0026rarr; WB (mediation)\u003c/b\u003e: Academic motivation also had an indirect effect on well-being via academic outcomes (indirect β\u0026thinsp;=\u0026thinsp;0.55 * 0.40\u0026thinsp;=\u0026thinsp;0.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). Part of the reason motivated students are happier is because they achieve more, which in turn boosts well-being. However, even controlling for performance, motivation retained a direct effect on well-being (β\u0026thinsp;=\u0026thinsp;0.38), suggesting motivation influences well-being through other channels as well \u0026ndash; likely through daily enjoyment of learning and a sense of purpose.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBPN \u0026rarr; AM \u0026rarr; WB\u003c/b\u003e: As noted earlier, the effect of need satisfaction on well-being was fully mediated by academic motivation (and further by performance). The chain BPN \u0026rarr; AM \u0026rarr; WB was significant (β\u0026thinsp;\u0026asymp;\u0026thinsp;0.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Additionally, BPN satisfaction had an alternative path to WB through both AM and AO: BPN \u0026rarr; AM \u0026rarr; AO \u0026rarr; WB (β\u0026thinsp;\u0026asymp;\u0026thinsp;0.61\u003cem\u003e0.55\u003c/em\u003e0.40\u0026thinsp;=\u0026thinsp;0.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). Summing these, basic needs indirectly influence well-being considerably. This aligns with SDT\u0026rsquo;s claim that need fulfillment enhances well-being, and the model clarifies that for students, this occurs by first fostering motivation and success experiences.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePE and WB\u003c/b\u003e: Although the direct PE \u0026rarr; WB path was not significant, the indirect effect of perceived employability on well-being was tested through academic motivation. That indirect path (PE \u0026rarr; AM \u0026rarr; WB) was significant (β_indirect\u0026thinsp;=\u0026thinsp;0.31*0.38\u0026thinsp;=\u0026thinsp;0.12, 95% CI [0.06, 0.19]). This implies that one way students\u0026rsquo; employability outlook affects their well-being is by influencing how motivated and invested they are in their academic activities. A student confident in getting a job likely engages more in studies (as found with PE \u0026rarr; AM), which then contributes to feeling more fulfilled. It is also noted that perceived employability may reduce career-related anxieties (not directly measured here), which could positively affect well-being \u0026ndash; a pathway supported in other research (Petruzziello et al., 2022).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003ePost-hoc Analysis: Group Differences\u003c/h3\u003e\n\u003cp\u003eExploratory multi-group SEM analyses were conducted to see if the model differed by gender or academic discipline. Multi-group comparison by gender (male vs. female) revealed no significant differences in path coefficients; a model with all paths constrained equal fit well (Δχ\u0026sup2; was not significant), suggesting the structural relationships hold similarly for male and female students. Both groups showed the same pattern of significant effects. The sample was split roughly into STEM majors vs. non-STEM majors; again, the model was largely invariant, though it was observed that the PE \u0026harr; AM reciprocal link was slightly stronger among non-STEM students (perhaps because employability in non-technical fields might be perceived as more contingent on personal effort and networking). However, these differences were not statistically significant at the .05 level. Finally, year of study did not meaningfully alter the model either \u0026ndash; the relations were consistent from first-year through senior students, though mean levels of PE did increase with year (as expected, nearing graduation). These consistency checks bolster the robustness of the findings across subpopulations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2\u003c/b\u003e illustrates the validated SEM model with all significant standardized path coefficients. The figure highlights the reciprocal nature of the relationships among academic motivation, perceived employability, and academic outcomes, as well as the downstream influence on well-being. Basic need satisfaction emerges as an exogenous driver with wide-ranging indirect effects. The model indicates that by nurturing their basic psychological needs, students become more motivated; this motivation not only improves their academic success but also enhances their future career confidence and personal well-being.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study set out to integrate key motivational and outcome variables into a single explanatory model for university student development. Using a large sample of Greek undergraduates,it was examined how the fulfillment of basic psychological needs relates to academic motivation, and in turn how motivation connects to perceived employability, academic performance, and psychological well-being. Overall, the findings provide strong support for the proposed model and offer novel insights into the interplay between academic experiences and career outlooks in higher education. In this section, the implications of each main finding in light of existing literature are discussed, addressing potential limitations, and suggesting directions for future research and practice.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe Crucial Role of Basic Needs in Academic Motivation\u003c/h2\u003e \u003cp\u003eOne of the clearest findings was that satisfaction of autonomy, competence, and relatedness needs is a powerful predictor of academic motivation (β\u0026thinsp;=\u0026thinsp;0.61). This aligns perfectly with Self-Determination Theory (Deci \u0026amp; Ryan, 2000), reinforcing that when students feel their basic needs are met in the university context, they internalize more autonomous motivation for learning. For example, a student who feels autonomously supported by instructors, competent in mastering coursework, and socially connected with peers is far more likely to find inherent value and enjoyment in their studies. The result is consistent with prior studies in Western and non-Western settings (e.g., Del Valle et al., 2025; Liu et al., 2024) that show need satisfaction correlates with higher quality motivation and engagement. It extends those results by demonstrating this effect in Greek higher education\u0026mdash;a context that has its own cultural and institutional characteristics. Notably, Greek universities have traditionally large class sizes and lecture-based teaching, which could challenge autonomy and relatedness need fulfillment. Yet, the data suggest that even within this context, variations in perceived need support (perhaps through caring faculty or collaborative learning opportunities) significantly impact student motivation. Practically, this underscores the importance for educators and institutions to cultivate a need-supportive academic environment. Interventions might include training faculty in autonomy-supportive teaching methods (e.g., providing choice in assignments, acknowledging student perspectives), establishing competence-building feedback systems, and fostering a sense of community on campus. By doing so, universities can indirectly boost academic motivation with downstream benefits for performance and well-being. This study thereby adds to the evidence base advocating for student-centered learning climates in higher education (Jeno et al., 2018; Orsini et al., 2015).\u003c/p\u003e \u003cp\u003eInterestingly, it was found that basic need satisfaction did not have significant direct effects on academic performance or well-being once motivation was accounted for. Instead, its influence was fully channeled through academic motivation (and subsequent variables). This suggests that need satisfaction by itself may not raise grades or happiness unless it translates into greater self-determined motivation. In other words, it is the energized, purposeful behavior resulting from need satisfaction that yields tangible outcomes. This fully mediated pattern is consistent with SDT\u0026rsquo;s process view (Ryan \u0026amp; Deci, 2017), and empirically concurs with recent work by Gonz\u0026aacute;lez-Arias et al. (2025), who found BPN satisfaction indirectly affected grades via positive affect and motivation. It highlights a mechanism: need-supportive environments \u0026rarr; autonomous motivation \u0026rarr; better effort and coping \u0026rarr; higher achievement and well-being. For researchers, it reinforces treating motivation as a mediator in models of need effects; for practitioners, it means that simply giving resources or support is not enough\u0026mdash;students must internalize it (as motivation) for benefits to manifest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAcademic Motivation and the Feedback Loop with Performance and Employability\u003c/h2\u003e \u003cp\u003eConsistent with a wealth of literature, academic motivation strongly predicted academic performance (β\u0026thinsp;=\u0026thinsp;0.55). This finding echoes the sentiment that \u0026ldquo;motivation matters\u0026rdquo; for student success (Richardson et al., 2012; Robbins et al., 2004). Motivated students likely invest more time studying, use deeper learning strategies, and persist through difficulties (Pintrich, 1999; Kusurkar et al., 2013), leading to better academic results. The study contributes additional evidence in a Greek context, suggesting that initiatives to boost students\u0026rsquo; motivation (especially intrinsic motives for learning) could be an effective lever to improve academic outcomes in Greek universities. This is particularly relevant given concerns about prolonged study durations and high drop-out rates in some Greek higher education programs (OECD, 2020). By targeting motivation, educators can indirectly influence performance metrics. For instance, mentoring programs, goal-setting workshops, and value-reappraisal exercises have been shown to enhance student motivation and subsequently academic achievement (Harackiewicz et al., 2016; Schutte \u0026amp; Malouff, 2019).\u003c/p\u003e \u003cp\u003eBeyond the expected motivation \u0026rarr; performance link, the model revealed a reciprocal performance \u0026rarr; motivation effect (β\u0026thinsp;=\u0026thinsp;0.27). This two-way relationship aligns with the notion of a self-reinforcing cycle between achievement and motivation known in educational psychology (Marsh \u0026amp; Martin, 2011). Success in academics can boost one\u0026rsquo;s academic self-concept and self-efficacy, which then fuels further motivation (Bandura, 1997; Komarraju \u0026amp; Nadler, 2013). For example, getting a high grade on an exam may make a student more confident and interested in the subject, spurring them to engage even more deeply in future learning. While the cross-sectional data cannot confirm temporal causality, the significant bidirectional paths in SEM align with longitudinal research showing reciprocal effects between academic self-beliefs/motivation and achievement over time (Guay et al., 2003; Corpus et al., 2020). This finding suggests that interventions can potentially initiate positive spirals. Early academic successes (even small ones) could be leveraged to increase motivation which then leads to larger successes. Educational programs might focus on providing early feedback and mastery experiences for first-year students to kick-start this virtuous cycle.\u003c/p\u003e \u003cp\u003eOne of the novel contributions of this study is integrating \u003cb\u003eperceived employability\u003c/b\u003e into the academic motivation-performance dynamic. Clear evidence that academically motivated students tend to feel more employable (β\u0026thinsp;=\u0026thinsp;0.42) is found. This supports the idea that engaged students build human capital and networks that bolster their confidence in securing a job (Clarke, 2018; Tentama \u0026amp; Arridha, 2020). A motivated student likely participates in internships, takes leadership roles in projects, and attains skill certificates \u0026ndash; all of which enhance employability. Moreover, being motivated may simply make students more optimistic and proactive about career planning, thus raising their perceived employability (which often involves an element of optimism about job prospects). Crucially, the reverse path is also found: students who believed strongly in their employability had higher academic motivation (β\u0026thinsp;=\u0026thinsp;0.31). To the knowledge, this reciprocal relation has seldom been empirically demonstrated, especially in a single-time SEM. It resonates with findings by Bozgeyikli et al. (2023) who reported that students with greater confidence in their career opportunities showed higher academic effort and persistence, presumably because they see a clearer payoff to academic work. Theoretically, this can be framed in terms of expectancy-value models (Eccles \u0026amp; Wigfield, 2002): if a student expects that doing well in their studies will lead to a good job (high outcome expectancy), their task motivation should increase. High perceived employability might thus strengthen the perceived instrumentality of academic tasks for achieving career goals, thereby motivating students to invest in those tasks (Karimi \u0026amp; Sotoodeh, 2019).\u003c/p\u003e \u003cp\u003eThe interplay between motivation and employability has implications for how universities approach career services and academic advising. The results suggest that academic affairs and career services should not operate in isolation. Enhancing students\u0026rsquo; employability (through career counseling, resume workshops, employer networking events, etc.) might have a positive feedback on their academic engagement. When students feel their studies are leading somewhere tangible, they may approach coursework with greater vigor. Conversely, boosting students\u0026rsquo; academic motivation (through pedagogical improvements or mentoring) can improve not just grades but also students\u0026rsquo; outlook on their career readiness. This calls for an integrated strategy where academic support and career development are linked. For example, incorporating career-relevant projects into the curriculum or highlighting transferable skills gained in coursework could simultaneously satisfy academic requirements and increase students\u0026rsquo; sense of employability. The findings add empirical weight to calls for bridging academic learning and employability in higher education (Tomlinson, 2017; Artess et al., 2017).\u003c/p\u003e \u003cp\u003eIt is important to note that while a strong connection between perceived employability and academic motivation is found, the relationship between perceived employability and actual academic performance was weaker (the correlation was r\u0026thinsp;\u0026asymp;\u0026thinsp;.22, and no direct structural path was posited from PE to AO). This suggests that simply feeling employable does not automatically translate to better grades \u0026ndash; it influences performance largely via motivation. A student might be confident about getting a job through other experiences (social capital, etc.) and not necessarily have top grades. This nuance aligns with studies which find that employability perceptions correlate more with soft outcomes like self-efficacy and networking behaviors than with GPA (Rothwell et al., 2008). It reinforces that perceived employability is a distinct construct \u0026ndash; related to but not identical with academic success.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAcademic Outcomes and Student Well-Being\u003c/h2\u003e \u003cp\u003eThe model highlights academic success and motivation as significant contributors to students\u0026rsquo; well-being. Academic performance had a moderate positive effect on psychological well-being (β\u0026thinsp;=\u0026thinsp;0.40), even when controlling for other factors. This finding contributes to a growing body of evidence that doing well academically can enhance students\u0026rsquo; mental health and life satisfaction. One explanation is that academic success fulfills competence needs and provides a sense of accomplishment, which is inherently gratifying (Sheldon et al., 2019). Additionally, good performance may reduce academic stress (e.g., less worry about failing or retaking courses), thereby improving overall well-being. The result dovetails with Trucchia et al. (2013) who found higher well-being among high-performing students, and with survey research showing positive associations between college GPA and indicators of well-being and adjustment (Salanova et al., 2010). It is also consistent with the idea of \u0026ldquo;academic buoyancy\u0026rdquo; \u0026ndash; students who achieve academic goals have greater resilience and positive affect to handle other life challenges (Martin \u0026amp; Marsh, 2008). For Greek students, given the societal value placed on academic success and limited job opportunities, performing well can be a significant relief and source of pride, potentially explaining the boost to well-being.\u003c/p\u003e \u003cp\u003eAcademic motivation also independently predicted well-being (β\u0026thinsp;=\u0026thinsp;0.38). This underscores that being autonomously motivated \u0026ndash; finding joy and value in one\u0026rsquo;s studies \u0026ndash; is beneficial for mental health in its own right. A student who is studying out of genuine interest and personal endorsement likely experiences less internal conflict, more positive emotions, and a greater sense of purpose (Niemiec \u0026amp; Ryan, 2009). These factors contribute to subjective well-being. The results align with SDT research showing that autonomous motivation correlates with higher life satisfaction and lower burnout among students (Baker, 2004; Jungert et al., 2018). It extends this knowledge by quantifying the effect in a multivariate context; even considering academic outcomes, motivation had a unique positive link to well-being. This suggests that \u003cb\u003ehow\u003c/b\u003e students approach learning (their motivation) can matter as much as \u003cb\u003ehow much\u003c/b\u003e they achieve in determining their happiness. In practical terms, educational policies that emphasize student well-being should not only focus on academic support for higher grades but also on fostering a climate where learning is enjoyable and meaningful. Strategies like project-based learning, autonomy in course selection, and connecting coursework to students\u0026rsquo; personal goals could maintain or increase intrinsic motivation, which in turn keeps students psychologically healthier.\u003c/p\u003e \u003cp\u003eThe findings did not confirm a direct effect of perceived employability on well-being once other factors were controlled, yet the positive correlation (r\u0026thinsp;=\u0026thinsp;.34) and indirect effect via motivation suggest something important: believing one has good job prospects is associated with feeling better. It may be that perceived employability\u0026rsquo;s influence on well-being is partly captured by the fact that it spurs students to be more engaged (hence happier through motivation) and partly by unmeasured factors like reduced financial anxiety. The literature notes that perceived employability can buffer the impact of stressors on well-being (De Cuyper et al., 2012; Rothwell et al., 2015); for students, high perceived employability might mitigate the distress that comes with uncertainty about the future, thereby indirectly sustaining higher well-being. The sample being in pre-graduation years might not yet fully experience the stress of job searching, which could be why the direct effect was small here. Future longitudinal research following students into post-graduation employment could clarify how perceived employability during university relates to later mental health outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eWhile this study has multiple strengths \u0026ndash; including a large sample, use of validated measures, and a comprehensive SEM analysis \u0026ndash; certain limitations warrant caution and point to avenues for further research. First, the cross-sectional design limits causal inferences. Although the structural model was theoretically grounded and alternative models were tested, the analysis does not allow for a definitive determination of temporal precedence among the variables. The reciprocal effects detected (AM with PE and AO) should be interpreted carefully; longitudinal or cross-lagged panel studies are needed to verify these two-way influences over time. Future research could track students across several semesters to see if increases in motivation lead to later increases in perceived employability and vice versa. An ideal design would be a three-wave longitudinal study, measuring these constructs annually from freshman to senior year, which would allow cross-lagged SEM to disentangle directionality.\u003c/p\u003e \u003cp\u003eSecond, all data were based on self-reports, which introduces the potential for common method variance and self-report biases. The researcher attempted to mitigate this (anonymous survey, mixing item order, using established scales), and a Harman\u0026rsquo;s single-factor test did not indicate a general factor issue. Nonetheless, the relationships (especially between motivation, employability, and well-being) might be inflated by positive response tendencies or personality traits (optimism, etc.). Including external measures such as actual academic records (for GPA) or observer ratings could strengthen future studies. For example, it would be informative to incorporate instructor-rated motivation or an objective test of academic skills to complement self-reported motivation and outcomes. Additionally, perceived employability could be complemented with more objective indicators of employability, such as number of internships or proficiency in job-market skills, to see how those align with student perceptions and outcomes.\u003c/p\u003e \u003cp\u003eThird, the measure of academic outcomes was primarily GPA and satisfaction. GPA in different fields and universities might not be strictly comparable due to grading norms. The scores were standardized and also included satisfaction to partly counteract that. Still, future studies could examine other academic success metrics like credits earned, on-time graduation, or achievement relative to peers. Moreover, academic outcomes can be broadened beyond performance to include learning outcomes or skill acquisition, which were not directly measured. Similarly, well-being was measured as flourishing (positive functioning); inclusion of measures for negative outcomes (like anxiety or depression scales) could provide a fuller picture of student mental health. It would be valuable to know if motivation and need satisfaction protect against distress in addition to promoting positive well-being.\u003c/p\u003e \u003cp\u003eFourth, while the sample was diverse in majors, it was exclusively Greek students. Cultural and educational system factors may affect generalizability. In Greece, public universities are tuition-free and there is less continuous assessment during the semester compared to US or UK systems; this might influence student motivation dynamics. Also, the Greek job market has unique challenges that shape students\u0026rsquo; views of employability. Thus, replication in other countries is encouraged. It is anticipated that the core model (need satisfaction \u0026rarr; motivation \u0026rarr; outcomes \u0026rarr; well-being, with motivation \u0026harr; employability) would hold in many contexts, but the magnitude of effects might vary. For instance, in countries with lower youth unemployment, the link between motivation and employability perceptions might be weaker because students take employability for granted. Cross-cultural comparisons could be enlightening.\u003c/p\u003e \u003cp\u003eAdditionally, the investigation of perceived employability was cross-sectional; an interesting future direction is to see how university experiences affect employability perceptions and actual employment outcomes after graduation. A longitudinal design could test if academic motivation during college predicts not only perceived but actual employability (e.g., number of job offers, speed to employment) post-graduation, controlling for academic performance. This would bridge the gap between students\u0026rsquo; subjective outlooks and objective career outcomes.\u003c/p\u003e \u003cp\u003eFinally, although the model was comprehensive, there are other relevant constructs not included that could enrich understanding. For example, \u003cb\u003eacademic self-efficacy\u003c/b\u003e is closely tied to both motivation and performance (Zimmerman, 2000), and \u003cb\u003ecareer adaptability\u003c/b\u003e or \u003cb\u003ecareer planning\u003c/b\u003e behaviors could also play a role in employability and well-being (Savickas \u0026amp; Porfeli, 2012). Incorporating these could form an even more nuanced model. The researcher also did not explicitly model extrinsic vs. intrinsic motivation subtypes due to complexity, but doing so could reveal if intrinsic motivation is the main driver of the positive effects (as SDT would predict) while controlled motivations have different or negative relations. Similarly, need frustration (the opposite of need satisfaction) can predict ill-being (Bartholomew et al., 2011); an intriguing extension would be to examine need frustration among students and whether it leads to demotivation, poorer performance, and distress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Higher Education Practice\u003c/h2\u003e \u003cp\u003eDespite these limitations, the present findings carry several important implications for higher education policy and practice, particularly in the Greek context but also more broadly. First, the strong influence of basic psychological need satisfaction calls for universities to evaluate and improve how they support students\u0026rsquo; autonomy, competence, and relatedness. Faculty development programs can emphasize autonomy-supportive teaching (e.g., giving students choice in projects, encouraging self-initiation). Academic support services can bolster competence by helping students set optimal challenges and providing positive feedback. Student affairs can cultivate relatedness by promoting mentoring programs, study groups, and inclusive campus communities. The payoff for such efforts is likely multifaceted: as the model shows, need satisfaction can cascade into better motivation, performance, and student well-being. In an era where student mental health is a growing concern in universities worldwide, addressing environmental supports for basic needs might be a preventative approach to foster resilience and enthusiasm.\u003c/p\u003e \u003cp\u003eSecond, the finding that academic motivation and success enhance perceived employability suggests that academic and career advising should be interconnected. Universities might implement programs that explicitly link academic engagement to career development. For example, incorporating real-world projects or internships into curricula can simultaneously stimulate intrinsic motivation (by providing meaningful learning contexts) and improve students\u0026rsquo; job readiness. Career centers can collaborate with academic departments to identify academically disengaged students and provide career mentoring to help them see the relevance of their studies to future goals \u0026ndash; potentially reigniting their motivation. Additionally, providing students with feedback on how their academic progress contributes to skill sets valued by employers could boost their self-perceived employability. In Greece, where students may feel uncertain about job prospects, highlighting the connection between academic competencies and employment (perhaps via alumni testimonials or industry panels) could both motivate current study and alleviate future anxiety.\u003c/p\u003e \u003cp\u003eThird, the results concerning well-being underline that academic policies should not solely prioritize grades but also students\u0026rsquo; psychological welfare. Over-emphasis on performance without regard to motivation quality may backfire; a student could achieve high grades under pressure yet suffer burnout or poor well-being. Instead, focusing on cultivating intrinsic motivation might yield high performance alongside better well-being \u0026ndash; a win-win. Universities might thus consider incorporating well-being and motivation indicators into their institutional assessments of educational quality. For instance, student surveys could track not just satisfaction with courses but also how motivated and supported students feel, using those data to improve programs. The positive relationship between performance and well-being also suggests that helping students succeed academically (through tutoring, early interventions for struggling students, etc.) is likely beneficial for their mental health.\u003c/p\u003e \u003cp\u003eLastly, the study has relevance for educators in framing the narrative of higher education to students. By demonstrating empirically that academic motivation and success contribute to feeling prepared for the job market and to personal happiness, an encouraging message is provided: engaging deeply in one\u0026rsquo;s education is not only about grades \u0026ndash; it has broader payoffs for one\u0026rsquo;s future and quality of life. Communicating this to students could potentially create a more internally driven student body. In settings like Greece, where external challenges (economic difficulties) might dampen student morale, emphasizing internal growth and linking it to future opportunities can be empowering.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the present research offers an integrative understanding of how psychological, academic, and career-related factors jointly influence university students\u0026rsquo; outcomes. The structural model \u0026ndash; supported by data from Greek higher education \u0026ndash; underscores that fulfilling students\u0026rsquo; basic psychological needs is the foundation for robust academic motivation, which in turn drives both superior academic performance and a confident outlook on employability. These academic gains and career confidence are not attained at the expense of personal well-being, but rather coincide with enhanced well-being. In fact, academic motivation and success emerge as significant contributors to students\u0026rsquo; psychological flourishing. The complex interrelations found (including reciprocal effects between motivation, performance, and employability perceptions) highlight that a student\u0026rsquo;s academic journey and career trajectory are deeply intertwined aspects of their overall development.\u003c/p\u003e \u003cp\u003eBy bridging academic motivation theory (SDT) with career development constructs in a single model, this study contributes to a more holistic view of student development in higher education. The findings encourage educators and policymakers to adopt a dual-focus approach: promoting academic excellence hand-in-hand with nurturing the internal motivational and emotional conditions that make such excellence sustainable and meaningful. As higher education faces evolving challenges \u0026ndash; from student mental health crises to demands for graduate employability \u0026ndash; the results advocate for strategies that do not treat these issues in isolation. Instead, the key may lie in creating enriching educational environments that simultaneously inspire students to learn, equip them with competencies for the future, and support their well-being. Students who are excited about learning and feel supported are likely to perform better, foresee a brighter career, and thrive personally. In essence, the path to producing successful and well-rounded graduates may begin with something as simple, yet profound, as ensuring students truly \u003cem\u003ewant\u003c/em\u003e to learn and feel good about doing so.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.M. wrote the main manuscript, conducted the statistical analysis. The whole manuscript was conducted and prepared by L.M.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData cannot be shared openly but may be available on request from authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArtemis, K., \u0026amp; Kounenou, K. 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Self-efficacy: An essential motive to learn. \u003cstrong\u003eContemporary Educational Psychology, 25\u003c/strong\u003e(1), 82\u0026ndash;91.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6813096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6813096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eStudent motivation in higher education is critically linked to academic success and personal well-being. Drawing on Self-Determination Theory, this study examines how basic psychological need satisfaction, academic motivation, and perceived employability interrelate and influence students\u0026rsquo; academic performance and well-being.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional survey of \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;701 Greek undergraduate students was conducted. Standardized questionnaires assessed basic psychological need (BPN) satisfaction, academic motivation (AM), perceived employability (PE), academic outcomes (AO; self-reported academic performance), and well-being (WB). Structural equation modeling (SEM) tested a hypothesized model in which BPN satisfaction fosters academic motivation and perceived employability, which in turn enhance academic performance and well-being.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDescriptive analyses indicated high internal reliabilities for all multi-item scales (α\u0026thinsp;=\u0026thinsp;.84\u0026ndash;.92). Bivariate correlations supported the theoretical links (e.g., BPN satisfaction was positively correlated with academic motivation, \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;.50, and with well-being, \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The SEM showed excellent fit (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.18, CFI\u0026thinsp;=\u0026thinsp;.959, TLI\u0026thinsp;=\u0026thinsp;.942, RMSEA\u0026thinsp;=\u0026thinsp;.041). As hypothesized, academic motivation was positively predicted by basic need satisfaction (β\u0026thinsp;=\u0026thinsp;0.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), perceived employability (β\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and academic performance (β\u0026thinsp;=\u0026thinsp;0.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Academic motivation in turn positively predicted perceived employability (β\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), academic performance (β\u0026thinsp;=\u0026thinsp;0.55, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and psychological well-being (β\u0026thinsp;=\u0026thinsp;0.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Academic performance also had a direct positive effect on well-being (β\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Together, the model explained 56% of the variance in academic motivation and 48% in well-being.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings underscore the pivotal role of satisfying students\u0026rsquo; autonomy, competence, and relatedness needs in fostering adaptive outcomes. Supportive learning environments that enhance intrinsic academic motivation not only improve academic success but also heighten students\u0026rsquo; confidence in their employability and their overall psychological well-being. Interventions aimed at need satisfaction and motivation may yield dual benefits for educational and career development outcomes in university students.\u003c/p\u003e","manuscriptTitle":"Academic Motivation, Perceived Employability, Academic Outcomes, and Well-Being in Greek Higher Education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 17:46:05","doi":"10.21203/rs.3.rs-6813096/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T12:03:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T05:18:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218195360032448084032862800709180147458","date":"2025-09-23T06:10:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T11:25:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302525724197004357798463540024288199963","date":"2025-06-30T15:26:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221492468990516270822784256377184237087","date":"2025-06-25T12:43:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-25T06:11:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-25T06:07:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-16T11:15:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-13T15:39:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Psychology","date":"2025-06-13T14:52:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"509fccfe-3799-49c4-b2e1-141d66cc0d52","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T12:54:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-30 17:46:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6813096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6813096","identity":"rs-6813096","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}