Demographic, Motivational, and Institutional Factors Impacting Academic Success in Higher Education

Demographic, Motivational, and Institutional Factors Impacting Academic Success in 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 Demographic, Motivational, and Institutional Factors Impacting Academic Success in Higher Education Patra Vlachopanou, Laura Maska, Dimitrios Kalamaras This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7364685/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study explores how various factors-including motivation, emotion, demographics, and institutional characteristics-interact to shape academic success among Greek university students. Based on Self-Determination Theory (SDT) and Tinto’s model of integration, it fills a gap in research by addressing the specific characteristics of the Greek higher education system. While prior research emphasizes the importance of motivation and integration, few studies have combined these with factors like program alignment, student type, and gender in a structural model. Methods A sample of 284 students, aged 18-28, completed validated Greek versions of the AMS, PASS, and SACQ. Structural Equation Modeling (SEM) was used to assess both direct and indirect effects on academic success. Key variables included gender, traditional vs. non-traditional student status, first-choice program enrollment, intrinsic and extrinsic motivation, academic and social integration, emotional adjustment, institutional attachment, and procrastination. Results Gender (female) was the strongest predictor of academic success (β = .819), affecting outcomes through intrinsic motivation, emotional adjustment, and procrastination. Academic integration (β = .424) and traditional student status (β = .300) also significantly predicted GPA. Social integration had an indirect effect through academic engagement. Procrastination (β = -.228) and emotional maladjustment (β = -.143) were major obstacles. While selecting a first-choice program affected institutional attachment, it did not directly impact academic performance. Conclusion Academic success in Greek universities is influenced by a range of personal, motivational, and contextual factors. Improving integration, reducing procrastination, and fostering intrinsic motivation can boost academic outcomes. Interventions should consider gender and student pathways to be more effective. Figures Figure 1 Introduction University academic performance is shaped by a complex combination of cognitive, emotional, motivational, and social factors (Gkintoni et al. 2025 , Mulaudzi, 2023 ). Exploring the processes that influence students’ academic achievement has been central in higher education research, particularly within the context of Self-Determination Theory (SDT) (Ryan & Deci, 2000 ) and Tinto’s student integration model (Tinto, 1975 ). The present study extends these theoretical frameworks by integrating various students’ traits and psychological factors into a structural equation model (SEM) design to predict academic achievement, and this in turn to predict academic performance. To truly understand what influences academic performance, it is important to examine not only the psychological factors such as motivation and integration but also the social characteristics that shape students’ academic experience (Rožman et al., 2025 ). These include whether students enroll in their preferred academic program, their student status (traditional or non-traditional), and demographic dimensions like gender (Brozina et al., 2024 ). These background variables often serve as indirect influences on achievement by affecting motivation, levels of institutional engagement, and behavioral adjustment (Brozina et al., 2024 ; Ribeiro et al., 2019 ). However, the combined impact of these factors in a single framework has not been sufficiently explored – especially in countries like Greece, where access to education and academic pathways are heavily regulated. One of the first determinants of academic adjustment is whether there is an alignment between students’ academic goals and the programs they are accepted into (Vlachopanou et al., 2025b ). Research shows that admission into one’s preferred academic program enhances intrinsic motivation and commitment, leading to better academic performance (Gottfried, 2019 ). Greek studies confirm that students who choose their field of study deliberately tend to feel more satisfied, engaged, and clear about their goals. This alignment helps solidify their sense of identity and ownership, both of which are precursors of intrinsic motivation (Digelidis & Papaioannou, 1999; Vlachopanou et al., 2023 ). Another important factor is the distinction between traditional and non-traditional students. Traditional students, who follow a linear academic path immediately after secondary education, often exhibit more consistent educational narratives and a stronger sense of belonging (Burger, 2023; Montes, 2024 ). In contrast, non-traditional students - such as those who work full-time or return to education after a gap year - often struggle more with balancing their external responsibilities with their need to integrate into the academic environment (Evitts, 2022). In Greek tertiary education, research has indicated that mature students frequently experience higher emotional stress and a reduced connection to their academic identity (Katsarou & Chatzipanagiotou, 2025 ; Retsi, 2022 ). Gender is another factor that influences academic outcomes. A consistent body of literature has shown that female students tend to exhibit higher levels of intrinsic motivation, emotion regulation, and self-discipline, all of which contribute to better academic performance (Vlachopanou et al., 2025b ; Nasir et al., 2024 ). Balkis and Duru (2024) found that female students report greater academic persistence and lower levels of procrastination. These gender differences in motivation are often linked to early socialization experiences and a stronger internalization of academic values (Groza et al., 2024 ). At the core of the present model is the concept of intrinsic motivation, which is a key element of Self-Determination Theory (SDT). According to Ryan and Deci ( 2000 ), intrinsic motivation arises when students engage in learning for their own sake, driven by curiosity or genuine interest. In their meta-analysis of 40 years of research, Cerasoli et al. ( 2014 ) found that intrinsic motivation significantly predicted performance outcomes, especially when tasks were complex. In Greece, Daniilidou et al. ( 2025 ) demonstrated that intrinsically motivated university students tend to engage more deeply with their studies and show greater resilience in overcoming academic failure. Social and academic integration, as defined by Tinto ( 1975 ), plays an important role in connecting personal characteristics and academic performance. Students who develop supportive peer networks and establish meaningful relationships with faculty are more likely to stay at the university and perform well academically. Empirical evidence from both Greek and international studies supports this assertion (Katsarou & Chatzipanagiotou, 2025 ; Gintoni et al., 2025; Kori, 2017 ). For instance, a study by Santiago-Ramago et al. (2021) found that feelings of social belonging and academic integration significantly impacted performance in both primary and university-level students. Similarly, research by Dunbar et al. ( 2016 ) highlighted that academic integration, particularly through mentoring and collaborative learning environments, positively affects GPA and student retention. Emotional adjustment is also an important factor which - according to previous research - functions in two ways. While emotional stability and positive affect facilitate academic engagement, high levels of emotional distress can hinder learning and performance. Greek literature confirms that emotional dysregulation is associated with increased dropout risk and reduced self-efficacy (Dimitropoulou et al., 2021 ). Another significant factor is institutional attachment, referring to students’ emotional identification with their university. Students who feel valued and connected with their academic community tend to show greater resilience, stronger goal orientation, and a deeper long-term commitment to their studies (Özer et al., 2021 ). Recent research by Kritikou & Giovazolias ( 2022 ) indicates that a strong institutional connection not only boosts motivation but also helps protect against the negative impacts of stress and emotional struggles (Huang & Kou. 2025). This highlights the importance of creating emotionally supportive learning environments that foster identity development and psychological safety. An important inhibitory factor is academic procrastination, which - according to extensive research - predicts lower performance outcomes (Vlachopanou et Karagiannopoulou, 2022a; Vlachopanou & Karagiannopoulou, 2022b ). Procrastination, as a failure of self-regulation, has been extensively examined within the SDT framework (Vlachopanou et al., 2025a ; Vlachopanou et al., 2025b ). It tends to flourish in contexts of amotivation or extrinsic regulation, where students fail to internalize the value of academic tasks. In the Greek context, Vlachopanou ( 2020 ) found that procrastination is a major challenge among university students, often linked to academic anxiety and time management. Taken together, literature underscores that academic performance and success are influenced by the dynamic combination of motivational, emotional, and contextual variables (Choi, 2025 ). Factors such as gender, program choice alignment, and student typology may indirectly influence performance by shaping students’ motivation, level of institutional integration, and self-regulatory factors (Vlachopanou et al., 2025b ; Balkis & Duru, 2024; Özer et al., 2021 ). Meanwhile, obstacles such as academic procrastination and emotional maladjustment can undermine progress, especially in environments that fail to support psychological needs for autonomy, competence, and relatedness (Vlachopanou et al., 2025a ; Vlachopanou et al., 2025b ). Within this context, the present study proposes a comprehensive conceptual model that brings together key constructs from Self-Determination Theory and student integration theory. By examining how demographic, psychological, and institutional factors connect to shape academic outcomes, this research aims to deepen our understanding of the processes that foster or hinder academic achievement in university settings. Grounded in international and Greek research, this study offers a theoretically informed framework that may contribute to future research suggesting practical interventions in higher education. The rationale of the study Understanding the determinants of academic performance in higher education is an important priority for educators, policymakers, and psychologists. While previous research has strongly highlighted the importance of factors like motivation, emotional adjustment, and a sense of belonging to the institution, there is still a lack of integrated, empirically tested models - especially in European and specifically Greek university contexts (Daniilidou et al., 2025; Katsarou & Chatzipanagiotou, 2025; Vlachopanou et al., 2025a; Vlachopanou et al., 2025b). This study builds on Self-Determination Theory (SDT) (Ryan & Deci, 2000) and Tinto’s model of student integration (1975), taking a multidimensional approach to academic performance by considering both personal and contextual factors. While SDT has been well-established in educational research, its interaction with factors like institutional attachment, emotional regulation, and behaviors such as procrastination has not been thoroughly explored in models that reflect the real-life complexity of university students’ experiences (Vlachopanou et al., 2025a; Vlachopanou et al., 2025b). Furthermore, existing models often overlook factors like initial academic alignment — such as enrolling in a first-choice program — and student type (traditional or non-traditional), both of which can significantly influence motivation and integration. The Greek higher education system has unique socio-academic features, such as centralized university admissions, strict department divisions, and high dropout rates in some programs, which require more research tailored to this specific area (Vlachopanou, 2020; Kalamaras et. Al. 2025b; Prokou et al., 2025). Despite the increasing attention to student well-being and engagement in Greek academic literature, there is a lack of comprehensive models that statistically test how various factors interact to predict academic performance among Greek university students. This study addresses this gap by examining how demographic (gender, student type), cognitive, emotional (intrinsic motivation, emotional adjustment), and contextual (social and academic integration, institutional attachment) factors come together to predict academic outcomes, including GPA and course completion. This study uses Structural Equation Modeling (SEM) to estimate both direct and indirect effects, providing deeper insights into how individual differences influence performance through mediating and moderating factors (Kalamaras et. al. 2025a, Hair et al., 2021; Ullman & Bentler, 2013). By including academic procrastination as a behavioral obstacle (Chatrakamollathas, Moha, & Choochom, 2022; Davari, Salehi b, Tabar, & Ebrahimi Moghadam, 2024) and first-choice program selection as a motivational enhancer (Falebita, Asanre, & Chibisa, 2025), it introduces underexplored yet practically significant dimensions. The use of latent constructs, validated through established scales, ensures both strong statistical accuracy and theoretical alignment (Hair et al., 2021). This study is particularly timely. In the post-pandemic academic world, where emotional instability and decreased motivation are more common among students (Kritikou & Giovazolias, 2022), it is crucial to identify which factors can be used to design more effective interventions. By understanding how certain student characteristics influence academic performance or challenges through motivational and emotional pathways, this model offers practical insights for faculty, advisors, and university leadership (Rožman et al., 2025). In the end, this study adds to the academic field by confirming that SDT and integration theory can be applied in a different cultural context, emphasizing the importance of factors like program alignment and academic procrastination, and providing practical, data-based suggestions to improve student retention and performance by focusing on intrinsic motivation and social integration. Summarizing the theoretical foundations and empirical gaps identified above - particularly regarding the interplay of motivation, emotional adjustment, institutional factors, and behavioral tendencies in the Greek higher education context -, the following hypotheses were formulated:: H1: Intrinsic motivation is positively associated with students’ academic performance (e.g., GPA and course completion). H2: Emotional adjustment positively influences academic performance, with intrinsic motivation acting as a mediating factor. H3: Academic procrastination negatively affects intrinsic motivation and academic performance. H4: Enrollment in a first-choice academic program predicts higher levels of motivation and academic integration. H5: Social and academic integration contribute positively to intrinsic motivation and academic performance. H6: Institutional attachment strengthens emotional adjustment and academic integration. H7: Student type (traditional vs. non-traditional) moderates the relationship between motivation and academic performance. H8: Gender differentiates the relationships among emotional adjustment, motivation, and academic performance. Method Participants The study sample consisted of 284 undergraduate students aged between 18 and 28 years (M = 21.2, SD = 1.7). Among them, 36.2% self-identified as male and 63.4% as female. Notably, the majority of participants (53.9%) are not traditional students, in the sense that they attendance the university studies at the earliest possible opportunity, immediately or up to two years after graduating from high school. For 48.6% of respondents, the academic department in which they were enrolled represented their first choice during the admission process. (see Table 1 ). Procedure Data collection was carried out via an online survey administered through Microsoft Survey Forms. Participation was voluntary and anonymous and was facilitated by the academic administrations of universities located in Attica and other provincial areas. All procedures adhered to established ethical standards and data protection protocols in accordance with institutional and regulatory guidelines. Measures Academic Motivation Scale (AMS) Academic motivation was assessed using the Academic Motivation Scale (AMS, AMS-C-28; Vallerand et al., 1992), a 28-item self-report measure based on a Likert-type format. The Greek version, adapted by Tsorbatzoudis et al. (2001), encompasses seven subscales measuring three types of intrinsic motivation (to know, to accomplish, and to experience stimulation), three types of extrinsic motivation (external regulation, introjected regulation, and identified regulation), and amotivation. Items are rated on a 5-point scale ranging from 0 (does not correspond at all) to 7 (corresponds exactly), with higher subscale scores reflecting stronger motivational tendencies. Internal consistency estimates indicated Cronbach's α = .89 for intrinsic motivation, α = .73 for extrinsic motivation, and α = .86 for amotivation (Fairchild, Horst, Finney, & Barron, 2005). Procrastination Assessment Scale – Students (PASS) Academic procrastination behaviors were measured using the Procrastination Assessment Scale – Students (PASS; Solomon & Rothblum, 1984), translated into Greek by Chatzidimou (1994). The scale evaluates students' tendency to delay academic tasks across five domains: (1) writing term papers; (2) preparing for exams; (3) administrative academic duties; (4) attending lectures; and (5) general academic functioning. Responses are given on a 5-point Likert scale (1 = never procrastinate, 5 = always procrastinate). In line with recommendations by Vlachopanou et al. (2022; 2023 ), only the first item of each subscale was used for analysis. The scale demonstrated high internal consistency with Cronbach's α = .88. Student Adaptation to College Questionnaire (SACQ) Adaptation to college life was measured using the Student Adaptation to College Questionnaire (SACQ; Baker & Siryk, 1989), adapted for Greek populations by Gkatona (2007). This instrument consists of 67 items rated on a 9-point Likert scale (1 = completely disagree, 9 = completely agree). The SACQ assesses four domains of adjustment: academic, institutional, social, and personal-emotional. Participants responded based on their recent collegiate experiences. The scale yielded excellent internal consistency with Cronbach's α = .93. Statistical Analysis The application of the aforementioned path models provided enough evidence to validate the conceptual framework and also provided empirical support for the hypotheses. The analysis not only elucidated the direct and indirect effects of students’ characteristics on their GPA and their academic adaptation, which took the role of criterion variables in each model, but they have also highlighted the complex interplay between students’ characteristics, on the aforementioned variables through their motivation status. For reasons of brevity, the full description of the independent variables as well as of those mentioned below, and their values are given in Table 1 together with descriptive and factor analysis results. As regards the dependent variable, a latent variable denoted by \(\:\eta\:,\) reflecting Academic Achievement.For the development of this latent constructs 3 indicator variables were measured. These variables are denoted as \(\:{\text{Y}}_{k}\) , \(\:k\:=\:\) 1,…,3 and they are also fully described along with the corresponding latent variable they measure in Table 1 . In particular Y 1 , Y 2 and Y 3 correspond to Grade point average (GPA), Grade point average (GPA) of the previous semester and Number of courses pending for the current semester, respectively Table 1 Variable definitions and values. Descriptive statistics, construct reliability (N = 268). Variables Values and Coding Percentage/Average Factor Loadings Cronbach's a/ Construct Reliability CR Predictor and mediator variables (Participants’ Characteristics) Age Ranging 18–28 11.2 years ( SD = 1.7) Gender (biological) 1, Male 0, Female 38.0% 62.0% Traditional Student 1, Yes 0, No 46.1% 53.9% The department the student enrolled was their first choice 1, Yes 0, No 48.6% 51.4% Intrinsic motivation - to know (subscale mean score from Academic Motivation Scale -AMS-C 28) Mean of 4 Likert Scales 1–7 M = 5.06 ( SD = 1.75) .891 Extrinsic motivation - identified (subscale mean score from Academic Motivation Scale -AMS-C 28) Mean of 4 Likert Scales 1–7 M = 5.35 ( SD = 1.52) .731 Academic Integration (SACQ subscale score) Sum of 8 Likert Scales 1–9 Ranging [9–594] M = 141.33 (SD = 23.58) .731 Social Integration (SACQ subscale score) Sum of 12 Likert Scales 1–9 Ranging [9–594] M = 103.84 (SD = 23.55) .885 Criterion variable \(\:\varvec{\eta\:}:\:\) Academic Achievements .989 Y 1 : Grade point average GPA Scale [0–10] Y 2 : Grade point average GPA, of the previous semester Scale [0–10] Y 3 : Number of courses pending for the current semester Ranging 0–22 Results A path analytic model using SEM was employed to investigate how key demographic, behavioral, and emotional constructs contribute to academic achievement among university students. Standardized effects are presented to assess both direct and mediated pathways (see. Figure 1 and Table 2 ). More specifically Gender (Female Students) demonstrated the strongest total influence on academic achievement (β = .819). The direct effect (β = .590) was substantial, reflecting gender-based differences in learning behavior and adaptation. The indirect effect (β = .229) was mediated through several constructs: intrinsic motivation toward integration (β = .383), intrinsic motivation to know (β = .420), personal emotional adaptation (β = –.152), procrastination (β = –.269), amotivation (β = –.119), and institutional attachment (β = .237). These mediators indicate that gender impacts motivation, emotional resilience, and behavioral regulation, all of which contribute to academic performance. Traditional Student Status was positively associated with achievement, exerting a direct effect (β = .214) and an indirect effect (β = .085). Indirect pathways included academic integration (β = .126) and procrastination (β = –.139). The total effect (β = .300) suggests that traditional students, possibly characterized by consistent enrollment patterns and fewer external obligations, are more likely to experience stable academic trajectories. First Choice Department Selection had no measurable direct effect but exerted a modest indirect effect (β = .022) via institutional attachment (β = .208). Although limited in magnitude, this suggests that students who enroll in their preferred department may feel more connected to the institution, supporting performance. Social Integration influenced academic achievement indirectly (β = .239) through academic integration (β = .563). While no direct path to achievement was specified, the strength of this mediated route emphasizes that peer and community engagement facilitates deeper academic involvement, which in turn enhances performance. Academic Integration was among the most impactful direct predictors (β = .424). This construct encompasses the degree to which students engage with coursework, develop learning routines, and connect intellectually with their studies. Institutional Attachment showed a smaller but positive direct effect (β = .103), indicating that a sense of belonging can enhance persistence and focus, although its mediating role may be more influential in broader models. Personal Emotional Adaptation had a negative direct effect (β = –.143), suggesting that students experiencing emotional distress or difficulty adapting may struggle academically, aligning with existing literature on adjustment and academic functioning. Finally, Procrastination was strongly and negatively associated with achievement (β = –.228), further supporting its role as a critical barrier to academic success. This aligns with cognitive-behavioral frameworks suggesting that delay and avoidance undermine performance outcomes. These findings reinforce the importance of motivational, emotional, and institutional factors in shaping academic outcomes. Variables such as gender and traditional student status exert significant influence, both directly and indirectly, while constructs like integration and procrastination highlight the behavioral dynamics underlying academic success. Table 2 Standardized Direct, Indirect, and Total Effects on Academic Achievement Predictor Direct Effect Indirect Effect Indirect Path Total Effect Gender (Female students) 0.590 0.229 Through: Intrinsic motivation - toward (0.383), Intrinsic motivation - toward (0.420), Personal Emotional Adaptation (-0.152), Procrastination - PASS (-0.269), Amotivation (-0.119 and Institutional Attachment (0.237) 0.819 Traditional Student .214 0.085 Through: Academic Integration (0.126) and Procrastination -PASS (-0.139) 0.300 The department the students enrolled was their first choice 0.022 Through: Institutional Attachment (0.208) 0.022 Social Integration 0.239 Through: Academic Integration (0.563) 0.239 Academic Integration 0.424 Attachment to institution 0.103 — Personal Emotional Adaptationt -0.143 — Procrastination -0.228 — Note : All coefficients are standardized beta weights derived from SEM analysis. Indirect effects were calculated based on mediated pathways across latent constructs. All coefficients are statistically significant in 0.05 CL. Discussion This study provides a comprehensive, research-based model of academic achievement among university students by examining the interaction of demographic, motivational, behavioral, and institutional factors within the context of Greek higher education. The findings highlight that gender, particularly being female, was the strongest predictor of academic success. This influence was both direct and indirect, through factors like intrinsic motivation, emotional adjustment, institutional adjustment, and self-regulatory behaviors like procrastination. The study also showed that both social and academic integration play a significant role in academic achievement, primarily through indirect pathways that support well-established theories of student persistence and engagement. Intrinsic motivation emerged as a key factor in academic performance, supporting the principles of Self-Determination Theory (SDT), while extrinsic motivation had little direct impact. Additionally, emotional maladjustment and procrastination were found to negatively affect academic outcomes, highlighting the importance of psychological resilience and effective behavioral self-regulation in achieving student success. Gender and Academic Performance The strong influence of gender as a predictor aligns with a large body of research showing that female students often exhibit higher levels of intrinsic motivation, emotional regulation, and academic persistence (Nasir et al., 2024 ; Vlachopanou et al., 2025b ). These traits are central to SDT, which argues that autonomy, competence, and relatedness needs are shaped by sociocultural and psychological contexts (Ryan & Deci, 2000 ). This finding supports Groza et al., ( 2024 ), who highlight that female students internalized academic values lead to better self-regulation and less procrastination. However, not all studies agree on the extent of gender differences. Some suggest that factors like academic discipline, institutional culture, and prior academic preparation can influence gender effects (Clark et al., 2014 ), suggesting that future research should explore the role of these intersecting factors. Integration and Institutional Belonging Academic integration proved to be one of the strongest direct predictors of achievement, aligning with Tinto’s ( 1975 ) model, which highlights how active academic engagement and involvement in the curriculum drive persistence and performance. While social integration wasn’t directly linked to achievement, it showed a significant indirect effect through academic integration, suggesting that building peer relationships and participating in the university community can enhance intellectual engagement. This finding is supported by Santiago – Ramajo et al., (2021), who identified similar indirect effects, and by Dunbar et al., ( 2016 ), who noted that mentoring and collaborative environments improve GPA through increased academic and social self-efficacy. Traditional students-those on the structured and continuous educational paths-were also more likely to successfully integrate into university life, reflecting the findings of Montes ( 2024 ) and Burger (2023). Institutional attachment had a smaller but still important direct effect. Research by Ozer et al., (2021) and Huang and Kou ( 2025 ) suggests that emotional connection to the institution promotes psychological safety and boosts motivation during stressful times, though its impact tends to be stronger when mediated by other factors. Motivation Profiles and Self-Determination Theory The study found that intrinsic motivation, especially the desire to learn and socially connect, played a key role in driving academic performance. This finding strongly supports the principles of SDT, which suggest that intrinsic motivation leads to greater engagement, higher persistence, and improved psychological well-being (Cerasoli et al., 2014 ; Ryan & Deci, 2000 ). On the other hand, amotivation and academic procrastination had negative impacts on achievement, which is consistent with previous research (Vlachopanou & Karagiannopoulou, 2022a ; Vlachopanou et al., 2025a ). These factors reflect a lack of self-regulation and low perceived competence, which hinder goal – setting and task initiation. The study also found that external motivation alone was not enough to drive success unless they were internalized and aligned with personal values-supporting the ideas presented by Gottfried ( 2019 ). Emotional Adaptation Emotional maladjustment had a direct negative impact on academic performance, backing previous research that shows emotional distress can hinder academic engagement, concentration, and resilience (Dimitropoulou et al., 2021 ). While emotional adaptation did not directly affect motivation in this model, it still plays a crucial role in academic success. This aligns with Kritikou and Giovazolias ( 2022 ), who highlight emotional regulation as a key factor in managing academic success and supporting student retention. Traditional vs. Non-Traditional Student Status Traditional students performed better than non-traditional students, both directly and indirectly, largely due to better integration and less procrastination. This supports previous research showing that students who follow a continuous academic path are more likely to develop a stable educational identity (Brozina et al., 2024 ; Evitts, 2022). While these findings highlight the advantages traditional students have, they also raise important questions about how institutions can better support non-traditional learners who face competing demands and challenges with integration. Academic Procrastination as an inhibitor Procrastination was found to be a significant negative predictor of academic performance, showing its role as a major barrier to success. This aligns with previous research that links procrastination to low intrinsic motivation and weak emotional coping skills (Davari et al., 2024 ; Chatrakamollathas et al., 2022 ). In Greece, Vlachopanou et al., ( 2025a ) highlight that procrastination is not only common but also strongly connected to emotional maladjustment and disengagement, making it a key area for potential interventions. Limitations and Future Research While the model is theoretically robust and supported by empirical data, there are few limitations to consider. The use of self-report measures could introduce biases like social desirability or recall issues. The cross-sectional design means we cannot draw conclusions about cause and effect, and although the sample is representative, it is primarily focused on Greece and Cyprus and does not include students from vocational institutions. Future research could build on this model by examining changes over time through a longitudinal approach. It would also be useful to explore how factors like academic discipline, financial background, and digital engagement might influence academic outcomes in the post-pandemic settings. Conclusion This study presents a well-rounded, statistically supported model of academic achievement among Greek university students, based on Self-Determination Theory and Tinto’s integration framework. The results highlight that academic success is influenced by a variety of factors, including motivation, emotional well-being, institutional engagement, and demographic context. Key predictors of success included being female, following a traditional education path, and strong academic integration, while emotional struggles and procrastination were identified as major obstacles. The study emphasizes the importance of comprehensive student support systems that not only focus on academic skills but also promote emotional resilience, reduce procrastination, and strengthen students’ sense of belonging to their institutions. By bringing together motivation, behavior, and institutional context in one unified model, this research provides valuable insights for educators, policymakers, and higher education professionals working to improve academic performance and student retention. Declarations E thics approval and consent to participate This study was conducted in accordance with the Aegean College/University of Essex ethical guidelines. Ethics approval was obtained from the AOC Ethics, approval number PGR-AOC—202310o23. Informed consent was obtained from all individual participants included in the study. Consent for publication Written informed consent for publication was obtained from all participants whose data or images are included in this article. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding No funding was received for this study. Authors' contributions Vlachopanou Patra: Conceptualization, methodology, data collection, and writing-original draft. Kalamaras Dimitrios: Data analysis, and writing. Laura Maska: Final approval of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank all the participants for their valuable contribution. Clinical trial number: not applicable. References Brozina C, Johri A, Chew A. (2024). A systematic review of research on nontraditional students reveals inconsistent definitions and a need for clarity: Focus on U.S.-based studies. Frontiers in Education, 9 , 1434494. https://doi.org/10.3389/feduc.2024.1434494 Burger, K. (2023). Disentangling the interplay of the sense of belonging and institutional channels in individuals’ educational trajectories. Developmental Psychology , 59 (1), 30. Cerasoli CP, Nicklin JM, Ford MT. 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Elsevier. https://doi.org/10.1016/bs.adms.2018.11.001 Groza IA, Ceobanu MC, Tofan CM. (2024). Motivational persistence and academic procrastination: The moderating role of behavioural deactivation for Romanian female students. European Journal of Psychology of Education, 39 , 3989–4001. https://doi.org/10.1007/s10212-024-00835-4 Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Danks, N. P. (2021). An introduction to structural equation modeling . In Partial least squares structural equation modeling (PLS-SEM) using R (pp. 1–25). Springer. https://doi.org/10.1007/978-3-030-80519-7_1. Huang H, Kou H. Learning agility, self-efficacy, and resilience as pathways to mental health in higher education: insights from a mixed-methods study. Front Psychol. 2025;16:1528066. https://doi.org/10.3389/fpsyg.2025.1528066 . Kalamaras D, Maska L, Nasika F. A proposed MIMIC structural equation model for assessing factors affecting time to degree—the case of the Greek tertiary system. Educ Sci. 2025a;15(2):187. 10.3390/educsci15020187 . Kalamaras D, Maska L, Nasika F. A Cox Proportional hazards model with latent covariates reflecting students’ preparation, motives, and expectations for the analysis of time to degree. Stats. 2025b;8(2):37. 10.3390/stats8020037 . Katsarou E, Chatzipanagiotou P. Examining the Association of Personality Traits and Grit on Greek Students’ Wellbeing in Higher Education. Educ Sci. 2025;15(1):57. Kori K. (2017). The role of academic, social and professional integration in predicting student retention in higher education information technology studies [Doctoral dissertation, Tallinn University]. Tallinn University Academic Library. Kritikou M, Giovazolias T. Emotion regulation, academic buoyancy, and academic adjustment of university students within a self-determination theory framework: A systematic review. Front Psychol. 2022;13:1057697. https://doi.org/10.3389/fpsyg.2022.1057697 . Montes A. So close and yet so far: Comparing the experiences of university non-traditional students before and after the pandemic. Eur Educational Res J. 2024;0(0). https://doi.org/10.1177/14749041241308971 . Mulaudzi IC. Factors affecting students’ academic performance: a case study of the university context. J Social Sci Policy Implications. 2023;11(1):18–26. Nasir F, Almuraikhi A, Alkusayer A, Saeed F, Alkushi A, Alowfi AS, AlGhaihab M, Nasir F. Assessing gender differences in the students’ academic performance, aptitude, emotional intelligence and grit. Middle East J Med Educ. 2024;7(1). https://doi.org/10.55890/2452-3011.1318 . Özer M, Özer A, Kocak A. Identification and emotional attachment in higher education: Antecedents and consequences. J Mark High Educ. 2021;31(1):1–25. https://doi.org/10.1080/08841241.2021.1936744 . Prokou E, Bagavos C, Charalampi A, Michalopoulou C. Greek undergraduate students: ‘Stagnant’, ‘perpetual’ or simply dropouts? Academia. 2025;0:39–40. https://doi.org/10.26220/aca.5251 . Retsi C. (2022). The role of the University: Stress and students [Full text in Greek]. https://doi.org/10.26220/aca.4023 Ribeiro L, Rosário P, Núñez JC, Gaeta M, Fuentes S. First-year students’ background and academic achievement: The mediating role of student engagement. Front Psychol. 2019;10:2669. https://doi.org/10.3389/fpsyg.2019.02669 . Rožman M, Vrečko I, Tominc P. Psychological Factors Impacting Academic Performance Among Business Studies’ Students. Educ Sci. 2025;15(2):121. https://doi.org/10.3390/educsci15020121 . Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68–78. https://doi.org/10.1037/0003-066X.55.1.68 . Santiago-Ramajo S, González-Andrade A, Quílez Robres A, Ortega Z. Intelligence quotient, short-term memory, and study habits as academic achievement predictors of elementary school: A follow-up study. Stud Educational Evaluation. 2021;70:101020. https://doi.org/10.1016/j.stueduc.2021.101020 . Tinto V. Dropout from Higher Education: A Theoretical Synthesis of Recent Research. Rev Educational Res Vol. 1975;45(1):89–125. Vlachopanou P, Maska L, Kalamaras D, Nasika F. The mediating role of academic procrastination in the link between academic motivation and academic adjustment among university students. Eur J Psychol Open. 2025a;84(1):33–45. https://doi.org/10.1024/2673-8627/a000074 . Vlachopanou P, Maska L, Kalamaras D, Nasika F. Impact of Gender, Age, First University Choice, and Residence on Academic Adaptation and Performance Through Academic Motivation: A Path Analysis. Eur J Psychol Open. 2025b;0(0). https://doi.org/10.1024/2673-8627/a000079 . Vlachopanou P, Karagianopoulou E, Ntritsos G. The Relationship Between Defenses and Learning: The Mediating Role of Procrastination and Well-Being Among Undergraduate Students. J Nerv Ment Dis. 2023;211(1):54–64. https://doi.org/10.1097/NMD.0000000000001570 . Vlachopanou P, Karagiannopoulou E. Defense Styles, Academic Procrastination, Psychological Wellbeing, and Approaches to Learning: A Person-Oriented Approach. J Nerv Ment Dis. 2022a;210(3):186–93. https://doi.org/10.1097/NMD.0000000000001423 . Vlachopanou P, Karagiannopoulou E. Defense styles, approaches to learning and the mediating role of academic procrastination. Sci J Pure Appl Sci. 2022b;10(2):990–1000. https://doi.org/10.14196/sjpas.v10i2.1712 . Vlachopanou P. (2020). The relationship between defense mechanisms and approaches to learning: The mediating role of academic procrastination and psychological well-being [Doctoral dissertation, University of Ioannina, School of Social Sciences, Department of Psychology]. University of Ioannina Repository. http://hdl.handle.net/10442/hedi/48177 Ullman JB, Bentler PM. (2013). Structural equation modeling. In I. B. Weiner, editor, Handbook of psychology: Vol. 2. Research methods in psychology (2nd ed., pp. 661–690). Wiley. https://doi.org/10.1002/9781118133880.hop202023 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7364685","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517451927,"identity":"632b3301-20f4-4562-93e7-ca63f4982aaa","order_by":0,"name":"Patra 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10:13:03","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":135669,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7364685/v1/657c574148cc27499a2006c9.html"},{"id":91843082,"identity":"c282a925-2290-4c12-9cc7-bb61be0a860f","added_by":"auto","created_at":"2025-09-22 10:05:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89774,"visible":true,"origin":"","legend":"\u003cp\u003eThe final SEM (direct and indirect effects)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7364685/v1/eb6eb713137fa284112df908.png"},{"id":99686933,"identity":"0b0cc2d3-d8e7-457e-abba-9a9bae28ccfa","added_by":"auto","created_at":"2026-01-07 09:40:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1205321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7364685/v1/2a5daddc-ddf0-44e4-8c8b-580b27cd367d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Demographic, Motivational, and Institutional Factors Impacting Academic Success in Higher Education","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUniversity academic performance is shaped by a complex combination of cognitive, emotional, motivational, and social factors (Gkintoni et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Mulaudzi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Exploring the processes that influence students\u0026rsquo; academic achievement has been central in higher education research, particularly within the context of Self-Determination Theory (SDT) (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Tinto\u0026rsquo;s student integration model (Tinto, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). The present study extends these theoretical frameworks by integrating various students\u0026rsquo; traits and psychological factors into a structural equation model (SEM) design to predict academic achievement, and this in turn to predict academic performance.\u003c/p\u003e\u003cp\u003eTo truly understand what influences academic performance, it is important to examine not only the psychological factors such as motivation and integration but also the social characteristics that shape students\u0026rsquo; academic experience (Rožman et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These include whether students enroll in their preferred academic program, their student status (traditional or non-traditional), and demographic dimensions like gender (Brozina et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These background variables often serve as indirect influences on achievement by affecting motivation, levels of institutional engagement, and behavioral adjustment (Brozina et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ribeiro et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the combined impact of these factors in a single framework has not been sufficiently explored \u0026ndash; especially in countries like Greece, where access to education and academic pathways are heavily regulated.\u003c/p\u003e\u003cp\u003eOne of the first determinants of academic adjustment is whether there is an alignment between students\u0026rsquo; academic goals and the programs they are accepted into (Vlachopanou et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). Research shows that admission into one\u0026rsquo;s preferred academic program enhances intrinsic motivation and commitment, leading to better academic performance (Gottfried, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Greek studies confirm that students who choose their field of study deliberately tend to feel more satisfied, engaged, and clear about their goals. This alignment helps solidify their sense of identity and ownership, both of which are precursors of intrinsic motivation (Digelidis \u0026amp; Papaioannou, 1999; Vlachopanou et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother important factor is the distinction between traditional and non-traditional students. Traditional students, who follow a linear academic path immediately after secondary education, often exhibit more consistent educational narratives and a stronger sense of belonging (Burger, 2023; Montes, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, non-traditional students - such as those who work full-time or return to education after a gap year - often struggle more with balancing their external responsibilities with their need to integrate into the academic environment (Evitts, 2022). In Greek tertiary education, research has indicated that mature students frequently experience higher emotional stress and a reduced connection to their academic identity (Katsarou \u0026amp; Chatzipanagiotou, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Retsi, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGender is another factor that influences academic outcomes. A consistent body of literature has shown that female students tend to exhibit higher levels of intrinsic motivation, emotion regulation, and self-discipline, all of which contribute to better academic performance (Vlachopanou et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Nasir et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Balkis and Duru (2024) found that female students report greater academic persistence and lower levels of procrastination. These gender differences in motivation are often linked to early socialization experiences and a stronger internalization of academic values (Groza et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt the core of the present model is the concept of intrinsic motivation, which is a key element of Self-Determination Theory (SDT). According to Ryan and Deci (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), intrinsic motivation arises when students engage in learning for their own sake, driven by curiosity or genuine interest. In their meta-analysis of 40 years of research, Cerasoli et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that intrinsic motivation significantly predicted performance outcomes, especially when tasks were complex. In Greece, Daniilidou et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) demonstrated that intrinsically motivated university students tend to engage more deeply with their studies and show greater resilience in overcoming academic failure.\u003c/p\u003e\u003cp\u003eSocial and academic integration, as defined by Tinto (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), plays an important role in connecting personal characteristics and academic performance. Students who develop supportive peer networks and establish meaningful relationships with faculty are more likely to stay at the university and perform well academically. Empirical evidence from both Greek and international studies supports this assertion (Katsarou \u0026amp; Chatzipanagiotou, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gintoni et al., 2025; Kori, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, a study by Santiago-Ramago et al. (2021) found that feelings of social belonging and academic integration significantly impacted performance in both primary and university-level students. Similarly, research by Dunbar et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) highlighted that academic integration, particularly through mentoring and collaborative learning environments, positively affects GPA and student retention.\u003c/p\u003e\u003cp\u003eEmotional adjustment is also an important factor which - according to previous research - functions in two ways. While emotional stability and positive affect facilitate academic engagement, high levels of emotional distress can hinder learning and performance. Greek literature confirms that emotional dysregulation is associated with increased dropout risk and reduced self-efficacy (Dimitropoulou et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother significant factor is institutional attachment, referring to students\u0026rsquo; emotional identification with their university. Students who feel valued and connected with their academic community tend to show greater resilience, stronger goal orientation, and a deeper long-term commitment to their studies (\u0026Ouml;zer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recent research by Kritikou \u0026amp; Giovazolias (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) indicates that a strong institutional connection not only boosts motivation but also helps protect against the negative impacts of stress and emotional struggles (Huang \u0026amp; Kou. 2025). This highlights the importance of creating emotionally supportive learning environments that foster identity development and psychological safety.\u003c/p\u003e\u003cp\u003eAn important inhibitory factor is academic procrastination, which - according to extensive research - predicts lower performance outcomes (Vlachopanou et Karagiannopoulou, 2022a; Vlachopanou \u0026amp; Karagiannopoulou, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Procrastination, as a failure of self-regulation, has been extensively examined within the SDT framework (Vlachopanou et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e; Vlachopanou et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). It tends to flourish in contexts of amotivation or extrinsic regulation, where students fail to internalize the value of academic tasks. In the Greek context, Vlachopanou (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that procrastination is a major challenge among university students, often linked to academic anxiety and time management.\u003c/p\u003e\u003cp\u003eTaken together, literature underscores that academic performance and success are influenced by the dynamic combination of motivational, emotional, and contextual variables (Choi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Factors such as gender, program choice alignment, and student typology may indirectly influence performance by shaping students\u0026rsquo; motivation, level of institutional integration, and self-regulatory factors (Vlachopanou et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Balkis \u0026amp; Duru, 2024; \u0026Ouml;zer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Meanwhile, obstacles such as academic procrastination and emotional maladjustment can undermine progress, especially in environments that fail to support psychological needs for autonomy, competence, and relatedness (Vlachopanou et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e; Vlachopanou et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWithin this context, the present study proposes a comprehensive conceptual model that brings together key constructs from Self-Determination Theory and student integration theory. By examining how demographic, psychological, and institutional factors connect to shape academic outcomes, this research aims to deepen our understanding of the processes that foster or hinder academic achievement in university settings. Grounded in international and Greek research, this study offers a theoretically informed framework that may contribute to future research suggesting practical interventions in higher education.\u003c/p\u003e"},{"header":"The rationale of the study","content":"\u003cp\u003eUnderstanding the determinants of academic performance in higher education is an important priority for educators, policymakers, and psychologists. While previous research has strongly highlighted the importance of factors like motivation, emotional adjustment, and a sense of belonging to the institution, there is still a lack of integrated, empirically tested models - especially in European and specifically Greek university contexts (Daniilidou et al., 2025; Katsarou \u0026amp; Chatzipanagiotou, 2025; Vlachopanou et al., 2025a; Vlachopanou et al., 2025b).\u003c/p\u003e\n\u003cp\u003eThis study builds on Self-Determination Theory (SDT) (Ryan \u0026amp; Deci, 2000) and Tinto’s model of student integration (1975), taking a multidimensional approach to academic performance by considering both personal and contextual factors. While SDT has been well-established in educational research, its interaction with factors like institutional attachment, emotional regulation, and behaviors such as procrastination has not been thoroughly explored in models that reflect the real-life complexity of university students’ experiences (Vlachopanou et al., 2025a; Vlachopanou et al., 2025b). Furthermore, existing models often overlook factors like initial academic alignment — such as enrolling in a first-choice program — and student type (traditional or non-traditional), both of which can significantly influence motivation and integration.\u003c/p\u003e\n\u003cp\u003eThe Greek higher education system has unique socio-academic features, such as centralized university admissions, strict department divisions, and high dropout rates in some programs, which require more research tailored to this specific area (Vlachopanou, 2020; Kalamaras et. Al. 2025b; Prokou et al., 2025). Despite the increasing attention to student well-being and engagement in Greek academic literature, there is a lack of comprehensive models that statistically test how various factors interact to predict academic performance among Greek university students. This study addresses this gap by examining how demographic (gender, student type), cognitive, emotional (intrinsic motivation, emotional adjustment), and contextual (social and academic integration, institutional attachment) factors come together to predict academic outcomes, including GPA and course completion.\u003c/p\u003e\n\u003cp\u003eThis study uses Structural Equation Modeling (SEM) to estimate both direct and indirect effects, providing deeper insights into how individual differences influence performance through mediating and moderating factors (Kalamaras et. al. 2025a, Hair et al., 2021; Ullman \u0026amp; Bentler, 2013). By including academic procrastination as a behavioral obstacle (Chatrakamollathas, Moha, \u0026amp; Choochom, 2022; Davari, Salehi b, Tabar, \u0026amp; Ebrahimi Moghadam, 2024) and first-choice program selection as a motivational enhancer (Falebita, Asanre, \u0026amp; Chibisa, 2025), it introduces underexplored yet practically significant dimensions. The use of latent constructs, validated through established scales, ensures both strong statistical accuracy and theoretical alignment (Hair et al., 2021).\u003c/p\u003e\n\u003cp\u003eThis study is particularly timely. In the post-pandemic academic world, where emotional instability and decreased motivation are more common among students (Kritikou \u0026amp; Giovazolias, 2022), it is crucial to identify which factors can be used to design more effective interventions. By understanding how certain student characteristics influence academic performance or challenges through motivational and emotional pathways, this model offers practical insights for faculty, advisors, and university leadership (Rožman et al., 2025).\u003c/p\u003e\n\u003cp\u003eIn the end, this study adds to the academic field by confirming that SDT and integration theory can be applied in a different cultural context, emphasizing the importance of factors like program alignment and academic procrastination, and providing practical, data-based suggestions to improve student retention and performance by focusing on intrinsic motivation and social integration.\u003c/p\u003e\n\u003cp\u003eSummarizing the theoretical foundations and empirical gaps identified above - particularly regarding the interplay of motivation, emotional adjustment, institutional factors, and behavioral tendencies in the Greek higher education context -, the following hypotheses were formulated::\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1:\u003c/strong\u003e Intrinsic motivation is positively associated with students’ academic performance (e.g., GPA and course completion).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2:\u003c/strong\u003e Emotional adjustment positively influences academic performance, with intrinsic motivation acting as a mediating factor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3:\u003c/strong\u003e Academic procrastination negatively affects intrinsic motivation and academic performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4:\u003c/strong\u003e Enrollment in a first-choice academic program predicts higher levels of motivation and academic integration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH5:\u003c/strong\u003e Social and academic integration contribute positively to intrinsic motivation and academic performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH6:\u003c/strong\u003e Institutional attachment strengthens emotional adjustment and academic integration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH7:\u003c/strong\u003e Student type (traditional vs. non-traditional) moderates the relationship between motivation and academic performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH8:\u003c/strong\u003e Gender differentiates the relationships among emotional adjustment, motivation, and academic performance.\u003c/p\u003e"},{"header":"Method","content":"\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThe study sample consisted of 284 undergraduate students aged between 18 and 28 years (M = 21.2, SD = 1.7). Among them, 36.2% self-identified as male and 63.4% as female. Notably, the majority of participants (53.9%) are not traditional students, in the sense that they attendance the university studies at the earliest possible opportunity, immediately or up to two years after graduating from high school. For 48.6% of respondents, the academic department in which they were enrolled represented their first choice during the admission process. (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eData collection was carried out via an online survey administered through Microsoft Survey Forms. Participation was voluntary and anonymous and was facilitated by the academic administrations of universities located in Attica and other provincial areas. All procedures adhered to established ethical standards and data protection protocols in accordance with institutional and regulatory guidelines.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eAcademic Motivation Scale (AMS)\u003c/b\u003e Academic motivation was assessed using the Academic Motivation Scale (AMS, AMS-C-28; Vallerand et al., 1992), a 28-item self-report measure based on a Likert-type format. The Greek version, adapted by Tsorbatzoudis et al. (2001), encompasses seven subscales measuring three types of intrinsic motivation (to know, to accomplish, and to experience stimulation), three types of extrinsic motivation (external regulation, introjected regulation, and identified regulation), and amotivation. Items are rated on a 5-point scale ranging from 0 (does not correspond at all) to 7 (corresponds exactly), with higher subscale scores reflecting stronger motivational tendencies. Internal consistency estimates indicated Cronbach's α\u0026thinsp;=\u0026thinsp;.89 for intrinsic motivation, α\u0026thinsp;=\u0026thinsp;.73 for extrinsic motivation, and α\u0026thinsp;=\u0026thinsp;.86 for amotivation (Fairchild, Horst, Finney, \u0026amp; Barron, 2005).\u003c/p\u003e\u003cp\u003e\u003cb\u003eProcrastination Assessment Scale \u0026ndash; Students (PASS)\u003c/b\u003e Academic procrastination behaviors were measured using the Procrastination Assessment Scale \u0026ndash; Students (PASS; Solomon \u0026amp; Rothblum, 1984), translated into Greek by Chatzidimou (1994). The scale evaluates students' tendency to delay academic tasks across five domains: (1) writing term papers; (2) preparing for exams; (3) administrative academic duties; (4) attending lectures; and (5) general academic functioning. Responses are given on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;never procrastinate, 5\u0026thinsp;=\u0026thinsp;always procrastinate). In line with recommendations by Vlachopanou et al. (2022; \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), only the first item of each subscale was used for analysis. The scale demonstrated high internal consistency with Cronbach's α\u0026thinsp;=\u0026thinsp;.88.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudent Adaptation to College Questionnaire (SACQ)\u003c/b\u003e Adaptation to college life was measured using the Student Adaptation to College Questionnaire (SACQ; Baker \u0026amp; Siryk, 1989), adapted for Greek populations by Gkatona (2007). This instrument consists of 67 items rated on a 9-point Likert scale (1\u0026thinsp;=\u0026thinsp;completely disagree, 9\u0026thinsp;=\u0026thinsp;completely agree). The SACQ assesses four domains of adjustment: academic, institutional, social, and personal-emotional. Participants responded based on their recent collegiate experiences. The scale yielded excellent internal consistency with Cronbach's α\u0026thinsp;=\u0026thinsp;.93.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe application of the aforementioned path models provided enough evidence to validate the conceptual framework and also provided empirical support for the hypotheses. The analysis not only elucidated the direct and indirect effects of students\u0026rsquo; characteristics on their GPA and their academic adaptation, which took the role of criterion variables in each model, but they have also highlighted the complex interplay between students\u0026rsquo; characteristics, on the aforementioned variables through their motivation status.\u003c/p\u003e\u003cp\u003eFor reasons of brevity, the full description of the independent variables as well as of those mentioned below, and their values are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e together with descriptive and factor analysis results.\u003c/p\u003e\u003cp\u003eAs regards the dependent variable, a latent variable denoted by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\eta\\:,\\)\u003c/span\u003e\u003c/span\u003e reflecting Academic Achievement.For the development of this latent constructs 3 indicator variables were measured. These variables are denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Y}}_{k}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\:=\\:\\)\u003c/span\u003e\u003c/span\u003e1,\u0026hellip;,3 and they are also fully described along with the corresponding latent variable they measure in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In particular Y\u003csub\u003e1\u003c/sub\u003e, Y\u003csub\u003e2\u003c/sub\u003e and Y\u003csub\u003e3\u003c/sub\u003e correspond to Grade point average (GPA), Grade point average (GPA) of the previous semester and Number of courses pending for the current semester, respectively\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\u003e\u003cem\u003eVariable definitions and values. Descriptive statistics, construct reliability (N\u0026thinsp;=\u0026thinsp;268).\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eValues and Coding\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePercentage/Average\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eFactor Loadings\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eCronbach's a/ Construct Reliability CR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictor and mediator variables (Participants\u0026rsquo; Characteristics)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRanging\u003c/p\u003e\u003cp\u003e18\u0026ndash;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.2 years\u003c/p\u003e\u003cp\u003e(\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender (biological)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1, Male\u003c/p\u003e\u003cp\u003e0, Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.0%\u003c/p\u003e\u003cp\u003e62.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraditional Student\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1, Yes\u003c/p\u003e\u003cp\u003e0, No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.1%\u003c/p\u003e\u003cp\u003e53.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe department the student enrolled was their first choice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1, Yes\u003c/p\u003e\u003cp\u003e0, No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.6%\u003c/p\u003e\u003cp\u003e51.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eIntrinsic motivation - to know\u003c/em\u003e (subscale mean score from Academic Motivation Scale -AMS-C 28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean of 4 Likert Scales 1\u0026ndash;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.06 \u003c/p\u003e\u003cp\u003e(\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.891\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eExtrinsic motivation - identified\u003c/em\u003e (subscale mean score from Academic Motivation Scale -AMS-C 28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean of 4 Likert Scales 1\u0026ndash;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.35 \u003c/p\u003e\u003cp\u003e(\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.731\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAcademic Integration (SACQ subscale score)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of 8 Likert Scales 1\u0026ndash;9\u003c/p\u003e\u003cp\u003eRanging \u003c/p\u003e\u003cp\u003e[9\u0026ndash;594]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eM\u0026thinsp;=\u0026thinsp;141.33 \u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(SD\u0026thinsp;=\u0026thinsp;23.58)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.731\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSocial Integration (SACQ subscale score)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of 12 Likert Scales 1\u0026ndash;9\u003c/p\u003e\u003cp\u003eRanging \u003c/p\u003e\u003cp\u003e[9\u0026ndash;594]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eM\u0026thinsp;=\u0026thinsp;103.84 \u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(SD\u0026thinsp;=\u0026thinsp;23.55)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.885\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCriterion variable\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\eta\\:}:\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eAcademic Achievements\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.989\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003csub\u003e1\u003c/sub\u003e: Grade point average GPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScale \u003c/p\u003e\u003cp\u003e[0\u0026ndash;10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003csub\u003e2\u003c/sub\u003e: Grade point average GPA, of the previous semester\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScale \u003c/p\u003e\u003cp\u003e[0\u0026ndash;10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003csub\u003e3\u003c/sub\u003e: Number of courses pending for the current semester\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRanging\u003c/p\u003e\u003cp\u003e0\u0026ndash;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA path analytic model using SEM was employed to investigate how key demographic, behavioral, and emotional constructs contribute to academic achievement among university students. Standardized effects are presented to assess both direct and mediated pathways (see. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). More specifically\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGender (Female Students)\u003c/b\u003e demonstrated the strongest total influence on academic achievement (β\u0026thinsp;=\u0026thinsp;.819). The \u003cb\u003edirect effect\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;.590) was substantial, reflecting gender-based differences in learning behavior and adaptation. The \u003cb\u003eindirect effect\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;.229) was mediated through several constructs: intrinsic motivation toward integration (β\u0026thinsp;=\u0026thinsp;.383), intrinsic motivation to know (β\u0026thinsp;=\u0026thinsp;.420), personal emotional adaptation (β = \u0026ndash;.152), procrastination (β = \u0026ndash;.269), amotivation (β = \u0026ndash;.119), and institutional attachment (β\u0026thinsp;=\u0026thinsp;.237). These mediators indicate that gender impacts motivation, emotional resilience, and behavioral regulation, all of which contribute to academic performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTraditional Student Status\u003c/b\u003e was positively associated with achievement, exerting a \u003cb\u003edirect effect\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;.214) and an \u003cb\u003eindirect effect\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;.085). Indirect pathways included academic integration (β\u0026thinsp;=\u0026thinsp;.126) and procrastination (β = \u0026ndash;.139). The total effect (β\u0026thinsp;=\u0026thinsp;.300) suggests that traditional students, possibly characterized by consistent enrollment patterns and fewer external obligations, are more likely to experience stable academic trajectories.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFirst Choice Department Selection\u003c/b\u003e had no measurable direct effect but exerted a modest \u003cb\u003eindirect effect\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;.022) via institutional attachment (β\u0026thinsp;=\u0026thinsp;.208). Although limited in magnitude, this suggests that students who enroll in their preferred department may feel more connected to the institution, supporting performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSocial Integration\u003c/b\u003e influenced academic achievement \u003cb\u003eindirectly\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;.239) through \u003cb\u003eacademic integration\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;.563). While no direct path to achievement was specified, the strength of this mediated route emphasizes that peer and community engagement facilitates deeper academic involvement, which in turn enhances performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAcademic Integration\u003c/b\u003e was among the most impactful \u003cb\u003edirect predictors\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;.424). This construct encompasses the degree to which students engage with coursework, develop learning routines, and connect intellectually with their studies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInstitutional Attachment\u003c/b\u003e showed a smaller but positive \u003cb\u003edirect effect\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;.103), indicating that a sense of belonging can enhance persistence and focus, although its mediating role may be more influential in broader models.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePersonal Emotional Adaptation\u003c/b\u003e had a \u003cb\u003enegative direct effect\u003c/b\u003e (β = \u0026ndash;.143), suggesting that students experiencing emotional distress or difficulty adapting may struggle academically, aligning with existing literature on adjustment and academic functioning.\u003c/p\u003e\u003cp\u003eFinally, \u003cb\u003eProcrastination\u003c/b\u003e was strongly and negatively associated with achievement (β = \u0026ndash;.228), further supporting its role as a critical barrier to academic success. This aligns with cognitive-behavioral frameworks suggesting that delay and avoidance undermine performance outcomes.\u003c/p\u003e\u003cp\u003eThese findings reinforce the importance of motivational, emotional, and institutional factors in shaping academic outcomes. Variables such as gender and traditional student status exert significant influence, both directly and indirectly, while constructs like integration and procrastination highlight the behavioral dynamics underlying academic success.\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\u003e\u003cem\u003eStandardized Direct, Indirect, and Total Effects on Academic Achievement\u003c/em\u003e\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDirect\u003c/p\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndirect\u003c/p\u003e\u003cp\u003e\u0026nbsp;Effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndirect Path\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (Female students)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThrough: Intrinsic motivation - toward (0.383), Intrinsic motivation - toward (0.420), Personal Emotional Adaptation (-0.152), Procrastination -\u003c/p\u003e\u003cp\u003ePASS (-0.269), Amotivation (-0.119 and Institutional\u0026nbsp;Attachment (0.237)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraditional\u0026nbsp;Student\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThrough: Academic Integration (0.126) and Procrastination -PASS (-0.139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe department the students enrolled was their first choice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThrough: Institutional Attachment (0.208)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial\u0026nbsp;Integration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThrough: Academic Integration (0.563)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademic\u0026nbsp;Integration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttachment\u003c/p\u003e\u003cp\u003eto institution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonal\u003c/p\u003e\u003cp\u003eEmotional\u003c/p\u003e\u003cp\u003eAdaptationt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProcrastination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e: All coefficients are standardized beta weights derived from SEM analysis. Indirect effects were calculated based on mediated pathways across latent constructs. All coefficients are statistically significant in 0.05 CL.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive, research-based model of academic achievement among university students by examining the interaction of demographic, motivational, behavioral, and institutional factors within the context of Greek higher education. The findings highlight that gender, particularly being female, was the strongest predictor of academic success. This influence was both direct and indirect, through factors like intrinsic motivation, emotional adjustment, institutional adjustment, and self-regulatory behaviors like procrastination. The study also showed that both social and academic integration play a significant role in academic achievement, primarily through indirect pathways that support well-established theories of student persistence and engagement. Intrinsic motivation emerged as a key factor in academic performance, supporting the principles of Self-Determination Theory (SDT), while extrinsic motivation had little direct impact. Additionally, emotional maladjustment and procrastination were found to negatively affect academic outcomes, highlighting the importance of psychological resilience and effective behavioral self-regulation in achieving student success.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eGender and Academic Performance\u003c/h2\u003e\u003cp\u003eThe strong influence of gender as a predictor aligns with a large body of research showing that female students often exhibit higher levels of intrinsic motivation, emotional regulation, and academic persistence (Nasir et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vlachopanou et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). These traits are central to SDT, which argues that autonomy, competence, and relatedness needs are shaped by sociocultural and psychological contexts (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This finding supports Groza et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who highlight that female students internalized academic values lead to better self-regulation and less procrastination.\u003c/p\u003e\u003cp\u003eHowever, not all studies agree on the extent of gender differences. Some suggest that factors like academic discipline, institutional culture, and prior academic preparation can influence gender effects (Clark et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), suggesting that future research should explore the role of these intersecting factors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIntegration and Institutional Belonging\u003c/h2\u003e\u003cp\u003eAcademic integration proved to be one of the strongest direct predictors of achievement, aligning with Tinto\u0026rsquo;s (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) model, which highlights how active academic engagement and involvement in the curriculum drive persistence and performance. While social integration wasn\u0026rsquo;t directly linked to achievement, it showed a significant indirect effect through academic integration, suggesting that building peer relationships and participating in the university community can enhance intellectual engagement.\u003c/p\u003e\u003cp\u003eThis finding is supported by Santiago \u0026ndash; Ramajo et al., (2021), who identified similar indirect effects, and by Dunbar et al., (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), who noted that mentoring and collaborative environments improve GPA through increased academic and social self-efficacy. Traditional students-those on the structured and continuous educational paths-were also more likely to successfully integrate into university life, reflecting the findings of Montes (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Burger (2023).\u003c/p\u003e\u003cp\u003eInstitutional attachment had a smaller but still important direct effect. Research by Ozer et al., (2021) and Huang and Kou (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) suggests that emotional connection to the institution promotes psychological safety and boosts motivation during stressful times, though its impact tends to be stronger when mediated by other factors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMotivation Profiles and Self-Determination Theory\u003c/h2\u003e\u003cp\u003eThe study found that intrinsic motivation, especially the desire to learn and socially connect, played a key role in driving academic performance. This finding strongly supports the principles of SDT, which suggest that intrinsic motivation leads to greater engagement, higher persistence, and improved psychological well-being (Cerasoli et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ryan \u0026amp; Deci, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, amotivation and academic procrastination had negative impacts on achievement, which is consistent with previous research (Vlachopanou \u0026amp; Karagiannopoulou, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Vlachopanou et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). These factors reflect a lack of self-regulation and low perceived competence, which hinder goal \u0026ndash; setting and task initiation. The study also found that external motivation alone was not enough to drive success unless they were internalized and aligned with personal values-supporting the ideas presented by Gottfried (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eEmotional Adaptation\u003c/h2\u003e\u003cp\u003eEmotional maladjustment had a direct negative impact on academic performance, backing previous research that shows emotional distress can hinder academic engagement, concentration, and resilience (Dimitropoulou et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While emotional adaptation did not directly affect motivation in this model, it still plays a crucial role in academic success. This aligns with Kritikou and Giovazolias (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who highlight emotional regulation as a key factor in managing academic success and supporting student retention.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eTraditional vs. Non-Traditional Student Status\u003c/h2\u003e\u003cp\u003eTraditional students performed better than non-traditional students, both directly and indirectly, largely due to better integration and less procrastination. This supports previous research showing that students who follow a continuous academic path are more likely to develop a stable educational identity (Brozina et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Evitts, 2022). While these findings highlight the advantages traditional students have, they also raise important questions about how institutions can better support non-traditional learners who face competing demands and challenges with integration.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAcademic Procrastination as an inhibitor\u003c/h2\u003e\u003cp\u003eProcrastination was found to be a significant negative predictor of academic performance, showing its role as a major barrier to success. This aligns with previous research that links procrastination to low intrinsic motivation and weak emotional coping skills (Davari et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chatrakamollathas et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Greece, Vlachopanou et al., (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e) highlight that procrastination is not only common but also strongly connected to emotional maladjustment and disengagement, making it a key area for potential interventions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Research\u003c/h2\u003e\u003cp\u003eWhile the model is theoretically robust and supported by empirical data, there are few limitations to consider. The use of self-report measures could introduce biases like social desirability or recall issues. The cross-sectional design means we cannot draw conclusions about cause and effect, and although the sample is representative, it is primarily focused on Greece and Cyprus and does not include students from vocational institutions.\u003c/p\u003e\u003cp\u003eFuture research could build on this model by examining changes over time through a longitudinal approach. It would also be useful to explore how factors like academic discipline, financial background, and digital engagement might influence academic outcomes in the post-pandemic settings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents a well-rounded, statistically supported model of academic achievement among Greek university students, based on Self-Determination Theory and Tinto\u0026rsquo;s integration framework. The results highlight that academic success is influenced by a variety of factors, including motivation, emotional well-being, institutional engagement, and demographic context. Key predictors of success included being female, following a traditional education path, and strong academic integration, while emotional struggles and procrastination were identified as major obstacles.\u003c/p\u003e\u003cp\u003eThe study emphasizes the importance of comprehensive student support systems that not only focus on academic skills but also promote emotional resilience, reduce procrastination, and strengthen students\u0026rsquo; sense of belonging to their institutions. By bringing together motivation, behavior, and institutional context in one unified model, this research provides valuable insights for educators, policymakers, and higher education professionals working to improve academic performance and student retention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Aegean College/University of Essex ethical guidelines. Ethics approval was obtained from the AOC Ethics, approval number PGR-AOC—202310o23. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from all participants whose data or images are included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003cP\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003cP\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVlachopanou Patra: Conceptualization, methodology, data collection, and writing-original draft.\u003c/p\u003e\n\u003cp\u003eKalamaras Dimitrios: Data analysis, and writing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLaura Maska: Final approval of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003cP\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the participants for their valuable contribution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrozina C, Johri A, Chew A. 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(2020). \u003cem\u003eThe relationship between defense mechanisms and approaches to learning: The mediating role of academic procrastination and psychological well-being\u003c/em\u003e [Doctoral dissertation, University of Ioannina, School of Social Sciences, Department of Psychology]. University of Ioannina Repository. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hdl.handle.net/10442/hedi/48177\u003c/span\u003e\u003cspan address=\"http://hdl.handle.net/10442/hedi/48177\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUllman JB, Bentler PM. (2013). Structural equation modeling. In I. B. Weiner, editor, \u003cem\u003eHandbook of psychology: Vol. 2. Research methods in psychology\u003c/em\u003e (2nd ed., pp. 661\u0026ndash;690). Wiley. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/9781118133880.hop202023\u003c/span\u003e\u003cspan address=\"10.1002/9781118133880.hop202023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7364685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7364685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study explores how various factors-including motivation, emotion, demographics, and institutional characteristics-interact to shape academic success among Greek university students. Based on Self-Determination Theory (SDT) and Tinto’s model of integration, it fills a gap in research by addressing the specific characteristics of the Greek higher education system. While prior research emphasizes the importance of motivation and integration, few studies have combined these with factors like program alignment, student type, and gender in a structural model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA sample of 284 students, aged 18-28, completed validated Greek versions of the AMS, PASS, and SACQ. Structural Equation Modeling (SEM) was used to assess both direct and indirect effects on academic success. Key variables included gender, traditional vs. non-traditional student status, first-choice program enrollment, intrinsic and extrinsic motivation, academic and social integration, emotional adjustment, institutional attachment, and procrastination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGender (female) was the strongest predictor of academic success (β = .819), affecting outcomes through intrinsic motivation, emotional adjustment, and procrastination. Academic integration (β = .424) and traditional student status (β = .300) also significantly predicted GPA. Social integration had an indirect effect through academic engagement. Procrastination (β = -.228) and emotional maladjustment (β = -.143) were major obstacles. While selecting a first-choice program affected institutional attachment, it did not directly impact academic performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcademic success in Greek universities is influenced by a range of personal, motivational, and contextual factors. Improving integration, reducing procrastination, and fostering intrinsic motivation can boost academic outcomes. Interventions should consider gender and student pathways to be more effective.\u003c/p\u003e","manuscriptTitle":"Demographic, Motivational, and Institutional Factors Impacting Academic Success in Higher Education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 10:04:58","doi":"10.21203/rs.3.rs-7364685/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dbfe3e14-1025-4345-92d6-4db5a08e5f30","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T09:40:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 10:04:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7364685","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7364685","identity":"rs-7364685","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}