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EI is a multidimensional construct with four interconnected yet distinctive dimensions: “Self-Emotion Appraisal (SEA), Others’ Emotion Appraisal (OEA), Use of Emotion (UOE), and Regulation of Emotion (ROE).” These four dimensions are fundamental to exploring how people can perceive, can process, and can manage emotional data, which consequently affects several aspects of personal life (Wong and Law, 2012). The first dimension of EI is SEA which describes the capability of perceiving and understanding one’s related emotions. This dimension forms the basis for efficient emotional and interpersonal connections (Salovey and Mayer, 1990; Javaid et al., 2024). This self-awareness aspect of EI permits people to mitigate stress, adapt to challenging conditions, and participate in goal-directed actions. These consequences of SEA aspect of EI are exceptionally significant for students with disabilities who frequently encounter extra academic and social challenges (Halpern, 1994; WenJing et al., 2021; Lifshitz, 2020). Goleman (2005) highlighted that SEA is crucial for personal development and QoL, as it permits people to comprehend their own emotions and adapt appropriately. Furthermore, empirical evidence has shown a positive relation between SEA and overall QoL. For example, the study conducted by Extremera and Fernández-Berrocal (2005) discovered that higher levels of perceived EI, including SEA, are correlated with higher levels of life satisfaction between higher education students. Additionally, the study by Alibabaie (2015) found a positive relationship between EI and QoL.

For students with disabilities, the competency to appraise one’s own emotions is focal in handling the extra challenges they encounter. Fields et al. (2024) emphasized that higher education students with disabilities can benefit from emotional wellness, which incorporates comprehending and handling their own emotions. This emotional ability can contribute to enhanced QoL by supporting better adapting strategies and social connections. SEA can also play a vital role in academic success (Suriá-Martínez et al., 2019). A meta-analysis study conducted by MacCann et al. (2020) argued that EI can predict positive academic outcomes, signaling that students who are skilled in understanding and handling their own emotions tend to accomplish better academic performance. Moreover, the study by Sánchez-Álvarez et al. (2016) found that EI is positively correlated with subjective QoL, which consequently can impact academic performance. Drawn on these arguments and the assumption discussed earlier from the Broaden-and-Build Theory of Positive Emotions, we can propose that:

OEA as the second dimension of EI, describes the people’s competency to perceive and comprehend the others’ emotions. This EI aspect can facilitate effective interpersonal interaction and the structuring of supportive connection (Salovey and Mayer, 1990). This competency is exceptionally vital for students with disabilities, who may encounter different challenges in their academic life. Goleman (2005) highlighted that empathy and social competencies, intimately related to OEA, are crucial for self-satisfaction and wellness. Empirical studies have confirmed the positive correlation from OEA to QoL. For example, the study conducted by Vasiou et al. (2024) found that the appraisal of others’ emotions can significantly contribute to wellness among university students and are mediated by psychological needs satisfaction. This signaled that OEA improved QoL by meeting basic psychological needs. Likewise, research has shown that people with higher level EI, containing robust OEA competency, demonstrated better levels of QoL (Alibabaie, 2015).

OEA as a dimension of EI can play a vital role in academic success. A meta-analysis study conducted by MacCann et al. (2020) confirmed that EI can predict positive academic outcomes. While the previous study did not purposely isolate OEA, the results underscored the importance of emotional skills in academic achievement. In the context of students with disabilities, EI, principally OEA, can assist in mitigating academic challenges by promoting recovery and adaptability. These conclusions are supported by results from a study conducted by Suriá-Martínez et al. (2019), which highlighted those students with higher level of EI profiles showed better self-concept, an element that is closely related to academic success. Drawn on these arguments and the assumption discussed earlier from the Broaden-and-Build Theory of Positive Emotions, we can propose that:

UOE, as an aspect of EI, describes the capacity to employee emotions to enable people cognitive activities (i.e., critical thinking, problem-solving, and decision-taking). This competency enables people to employee their emotional states to improve motivation and performance in several duties (1990). For students with disabilities, employing emotions may be predominantly beneficial in managing academic and social problems, thereby enhancing their overall QoL and academic performance. Previous research demonstrated that higher levels of EI, containing the ability to use emotions, are corelated with high levels of QoL. For example, a study conducted by Fatima et al. (2024) highlighted a significant positive path from EI to QoL among higher education students, signaling that emotional skills can contribute effectively to high level of life satisfaction and QoL. Likewise, providing emotional support has been found to mitigate the adverse effects of metal health disorder (i.e., stress and anxiety) on QoL among students with disabilities. In the same vein, a study conducted by Al-Shaer et al. (2024) supported the previous evidence and demonstrated that emotional support can have a moderating effect in the link from mental health disorders to QoL.

The capacity to use emotions productively is also related to a high level of academic success. A meta-analysis conducted by MacCann et al. (2020) suggested that EI is a substantial predictor of academic success, emphasizing that students who can successfully control and employee their emotions tend to obtain better academic consequences. These results emphasize the key role of emotional skills in accelerating the learning and academic accomplishments. Moreover, a study conducted by Bryant (2007), Hen and Goroshit (2014), Brabcova et al. (2015), and Saber (2016) on a sample of students with learning disabilities demonstrated a positive relationship between EI and academic outcomes, signifying that emotional skills can assist students with disabilities navigate academic problems more efficiently. Drawn on these arguments and the assumption discussed earlier from the Broaden-and-Build Theory of Positive Emotions, the hypotheses below can be suggested:

ROE, as a dimension of EI, describes the skill to control and modulate emotional responses in different situations, enabling people to adapt efficiently with stress, adjust to continuously changing circumstances, and sustain emotional balance (Salovey and Mayer, 1990). This ability is exceptionally fundamental for disabled students, who frequently encounter several academic challenges. Efficient emotion regulation can contribute to the student’s ability to resilience psychologically, have strong interpersonal connections, and improve well-being, all of which are essential to QoL and academic performance (Sacks and Kern, 2008; Munday and Horton, 2021). Previous research found a positive relationship between ROE and QoL. For example, a study conducted by Gavín-Chocano et al. (2020) demonstrated that among a sample of governmental workers with physical disabilities, higher levels of emotional regulation were positively and significantly correlated with improved quality of emotions in the workplace, signaling improved QoL. Likewise, emotional regulation has been found to have a link with better mental health consequences, which are directly linked to QoL (Shoshani and Steinmetz, 2014; Lifshitz, 2020). University students who can successfully regulate their emotions are competently equipped to manage the psychological needs of academic life, causing higher levels of life satisfaction and overall well-being (Gross and John, 2003). ROE as well can play a substantial role in academic success. A meta-analysis study conducted by MacCann et al. (2020) found that EI, involving the regulation of emotions, is a considerable predictor of academic success. University students who can control their emotions are more likely to remain concentrating, manage academic stress, and endure through continuous challenges, directly leading to improved academic outcomes. Drawn on these arguments and the assumption discussed earlier from the Broaden-and-Build Theory of Positive Emotions, the following hypotheses can be proposed:

QoL has several aspects, such as improved physical health, well psychological state, high level of independence, social connections, own beliefs, and correlation to salient attributes of the environment (World Health Organization, 1997). For university students with disabilities, these aspects can significantly impact their academic outcomes (Halpern, 1994; Torres and Vieira, 2014). A supportive and encouraging environment that improves QoL can positively influence academic outcomes. A study implemented at Qatar University evaluated the QoL of students with disabilities and correlation with academic adaptability and performance. The results indicated that higher levels of QoL were linked with better academic outcomes, which in turn can lead to improved academic performance (Al-Attiyah and Mahasneh, 2018). This signaled that improved QoL can lead to better academic performance for students with disabilities (Figure 1). Accordingly, we can propose that

The measures and variables employed were based on an extensive review of previous literature to select reliable and valid scales. To operationalize emotional intelligence (EI), the Wong and Law Emotional Intelligence Scale (WLEIS) (Wong and Law, 2012) was employed. WLEIS is a commonly considered self-report scale developed to measure emotional intelligence (EI) with four distinct and related dimensions (self-emotion appraisal, others’ emotion appraisal, use of emotion, and regulation of emotion). The scale has 16 items, four items for each EI dimension. Participants are asked to rate each item on a scale with a 7-point scale ranging from 1 “Strongly Disagree” to 7 “Strongly Agree.” Although the current study was conceptually directed from Salovey and Mayer’s (1990) ability model of emotional intelligence (EI), the Wong and Law Emotional Intelligence Scale (WLEIS) was used in the operationalization process. While performance-based scale (i, e. “the Mayer–Salovey–Caruso Emotional Intelligence Test” -MSCEIT) are more closely consistent with the ability model, they are regularly encountering a high cost, long time-intensiveness, and less practicable in large-scale or cross-section field research (Joseph and Newman, 2010; O’Connor et al., 2019). Conversely, the WLEIS was explicitly advanced employing the four-dimensions of the ability model “self-emotion appraisal, others’ emotion appraisal, use of emotion, and regulation of emotion” and has been widely validated in Middle Eastern and Asian settings (Law et al., 2004). Furthermore, meta-analytic studies confirmed the reliability and predictive validity of self-report EI measures, including WLEIS, in educational and organizational context (Miao et al., 2017). Moreover, the WLEIS has been widely revealed to be manageable, concise, and easy to administer, which is mainly beneficial when investigating populations that may require accessibility conditions (i.e., students with disabilities). Previous research papers have successfully used WLEIS among vulnerable or distinct samples, including people with health challenges and students that require inclusive educational context, arguing that the scale still reliable and valid in these cases (Chan, 2004; Extremera and Rey, 2016). Therefore, WLEIS was considered both theoretically adequate and culturally suitable for the current study.

Likewise, academic performance was operationalized by 3 items as suggested by Owusu-Acheaw and Larson (2015) and was found to be valid and reliable in several previous empirical studies (Elshaer et al., 2025; Zayed et al., 2024; Sobaih et al., 2022). Finally, QoL was operationalized by five items that reflected the “Satisfaction with Life Scale” (SWLS). This SWLS was developed by Diener et al. (1985) and described the perception of people’s cognitive judgments regarding overall life satisfaction. To ensure the face validity of the developed scale, the questionnaire was first reviewed by 10 academics who assessed the clarity, suitability and relevance, of the written questions. Likewise, a pilot test was implemented with 10 disabled King Faisal University (KFU) students. The replies from these two stages indicated that the questions were clearly readable and understood, with the minimum language modifications. These two steps ensured that the questionnaire showed adequate content and face validity. The full questionnaire was structured into 3 key parts. The initial section provided the targeted respondents with a clear and brief introduction that explained the study’s objectives, and to obtain a consent form to approve voluntary participation and guarantee the ethical criteria. The second section was structured to obtain main demographic information, such as type of participants’ disability, age, enrolled academic year, and respondents’ gender. The final part includes the study’s main latent factors. The study scale was originally established in English. To safeguard their appropriateness for usage in SA context, a translation and back-translation technique was conducted. First, 2- bilingual specialists separately translated the scale items into Arabic. Following, 2 other bilingual specialists, blinded to the first original versions, and back translated the scale items into English. Inconsistencies between the first original and the second back-translated forms were discussed, and very minor wording changes were made consequently.

The General Population and Housing Census conducted in 2024 in KSA stated that around 2% (1.8%) of the total population, representing around 64,800 residents, suffered from some type of disability. The different types of disabilities are hearing disability, mobility limitations, cognitive abilities, visual impairments, self-care challenges, and communication challenges. Conspicuously, higher education students have a significant ratio of this demographic, representing around 58% that are fully enrolled KSA public universities: King Abdulaziz University (1,569 full time students), King Saud University (663), Taibah University (523), Umm Al-Qura University (381), and King Faisal University (330). A convenience sampling approach was selected to collect the required data, and a statistical power analysis with G*Power 3.1 program was conducted to detect the minimum sample size. The results yielded that a minimum of 124 respondents was required to represent the total population adequately. The calculation was based on an effect size (f² = 0.15), with 5 predictors, and a significance p level of 0.05, and a requested power of 0.95. To facilitate data collection, 30 well trained enumerators were recruited to adequately adopt the ethical research policies, involving obtaining the informed consent form and ensuring the participant’s confidentiality. Out of 550 questionnaires distributed, 328 were retained to be valid, with a response rate of 59%. Invalid and incomplete forms were not analyzed and archived. The final dataset has roughly balanced gender distribution (52% female, 48% male) and students age was ranged from 17 to 24 years old; the highest percentage of students was suffered from mobility disability (50%) following visual impairment (23%), hearing impairments (15%) other disabilities (12%) accordingly. As the same participant answered the dependent and independents questions, common method variance (CMV) might be an issue (Lindell and Whitney, 2001). Therefore, we adopted several procedural and statistical remedies to make sure that CMV is not an issue in this research. The procedural steps included adopting various questions formats and order and respecting participants’ anonymity. The statistical remedies adopted with SPSS, Exploratory Factor Analysis (EFA) option, we exposed all items to “Harman’s Single Factor Test” to find out the amount of the variance that can be extracted in one single dimension, the result yielded a value of 41% variance extracted, indicating that CMV is not a problem in our dataset (Podsakoff et al., 2003).

This research followed the ethical guidelines of the Declaration of Helsinki. The research was approved by the Deanship of Scientific Research Ethical Committee, King Faisal University (project number: KFU-2025-ETHICS2820, date of approval: 6 September 2024). These guidelines were followed during the research design, data collection and data interpretation to protect the privacy of the respondents.

In our study, data analysis was guided by employing “Partial Least Squares Structural Equation Modeling” (PLS-SEM) with SmartPLS 4 software. The choice of this technique (PLS-SEM) was considered for several methodological advantages (Hair et al., 2021). It has several advantages over covariance-based SEM. It has fewer and less stringent data requirements compared to covariance-based SEM. First, given that our study is exploratory in its nature and has complex relationships between observed and latent variables, PLS-SEM is particularly adequate for assessing and predicting interrelationships between the study constructs (Leguina, 2015). Second, PLS-SEM has no rigorous assumptions about data distribution, making it adequate for non-normally distributed datasets (Hair et al., 2021). This merit makes PLS-SEM beneficial when dealing with real-world data that regularly demonstrates non-normal distributions (Chin, 1998). Third, PLS-SEM provides robust abilities for assessing and calculating mediating impact in the structural models (Leguina, 2015). This advantage is critical for understanding the mechanisms through which independent dimensions impact factors variables through mediators. Finally, compared to the commonly and widely used data analysis techniques that are based on covariance (i.e., CB-SEM), PLS-SEM is less sensitive to issues such as model identification problems, hence improving the reliability of the analytical results (Hair et al., 2017).

Table 1 presented the correlation matrix to inspect the relationships among all scale items. All values were in the expected directions, and none were excessively high or low (> 0.9 or <0.3), indicating that multicollinearity is improbably to be an issue in our study. Furthermore, multicollinearity was tested using Variance Inflation Factor (VIF). As shown in Table 2, all VIF scores ranged between 2.7 and 1.5, confirming the absence of multicollinearity concern (Tabachnick and Fidell, 2019) and consequently did not bias the path coefficients or inflate the standard errors. Additionally, Table 2 showed the descriptive statistics (means and standard deviations) of the study variables as well and demonstrated a proper variability and were within the predictable theoretical range of the scale items. Furthermore, to test the distribution of the data (normality), skewness and kurtosis scores were inspected. All scores were found to be within the adequate range of −2 to +2, signifying that the data did not deviate significantly from normality (Tabachnick and Fidell, 2019). Following the suggestions provided by Leguina (2015), the analysis process was conducted in two succeeding stages. The measurements model psychometric properties (Table 2) were inspected in stage one, while in stage two, the structural model was evaluated regarding hypothesis assessment.

The evaluation of the study measurement model encompassed assessment of convergent validity, discriminant validity, and reliability. All the study factor loadings are above 0.70, consistent with the threshold recommended by Fornell and Larcker (1981) and demonstrating high items’ reliability. Furthermore, both Cronbach’s alpha and composite reliability (C. R.) scores exceeded the value of 0.70, indicating adequate internal consistency (Gerbing and Anderson, 1988). The average variance extracted (AVE) results for each factor surpassed the value of 0.50, signaling a satisfactory convergent validity (see Table 2). Discriminant validity was evaluated employing the Fornell-Larcker metric, which requires that the square root of each factor’s AVE to be higher than its intercorrelations with other factors. All factors fulfilled this criterion, signaling adequate discriminant validity (see Table 3). Moreover, the heterotrait-monotrait ratio (HTMT) was inspected as per Henseler et al. (2009) suggestions, with all HTMT scores remaining below the 0.85 threshold to confirm discriminant validity (see Table 4). The cross-loadings values shown in Table 5 further confirmed the scale discriminant validity, where each item is highly loaded to its predetermined factor with no cross-loadings. These outcomes confirm the reliability and validity of the employed measurement model.

Several key criteria can be employed to evaluate the structural model predictive and explanatory capacities, as suggested by Chin (2010). The Stone-Geisser’s Q² value, reported in the blindfolding analysis, was inspected to test the model’s predictive relevance. All endogenous factors showed Q² scores above zero, specifically, QoL (0.693), and academic performance satisfaction (0.637), signaling that the study model can predict these factors effectively. Explanatory power was assessed via each endogenous factor’s coefficient of determination (R²) values. The R² scores were strong for QoL (0.704), and academic performance (0.666), suggesting that the study model can explain a large proportion of the variance in QoL and academic performance. The Model goodness of fit was further evaluated employing the “Standardized Root Mean Square Residual” (SRMR), the SRMR value was 0.067, which is below the suggested maximum value of 0.08, signaling a minimum discrepancy among the observed and predicted data. The NFI value (0.926) exceeded the recommended cut off point 0.90 and signaling a good GoF of the tested model and inferring that the specified structural hypothesized model adequately captures the underlying tested relationships among the study factors. These results confirm that the study’s structural model has robust predictive power, good explanatory power, and a satisfactory overall model fit, thus validating its capability for hypothesis testing (Figure 2).

After testing the reliability and validity of the developed measurement model and the designed structural model, the hypothesis testing phase can be initiated. As shown in Table 6 the path coefficient values, and their related t-values were extracted employing a bootstrapping approach with 5, 000 samples. Additionally, the significance of indirect paths was evaluated using the bootstrapping procedure with 5,000 resamples and a 95% bias-corrected confidence interval. This non-parametric method was chosen because it did not assume normality of indirect paths.

The PLS-SEM results indicated that self-emotional appraisal (as a dimension of emotional intelligence) was found to have a direct positive influence on student QOL (β = 0.174, t = 3.253, p < 0.01) and academic performance (β = 0.108, t = 1.972, p < 0.05), and a positive indirect impact on academic performance through QoL (β = 0.040, t = 2.793, p < 0.01) supporting H1, H2 and H3. Additionally, others’ emotion appraisal (as a dimension of emotional intelligence) showed a positive and significant direct impact on students’ QoL (β = 0.227, t = 6.220, p < 0.001), and academic performance (β = 0.125, t = 2.599, p < 0.01), and a positive indirect impact on academic performance through QoL (β = 0.052, t = 3.490, p < 0.001) this confirming H4, H5, and H6. Similarly, use of emotions (as a dimension of emotional intelligence) successfully impacted QOL directly (β = 0.334, t = 7.363, p < 0.001), supporting H7, but failed to significantly influence student academic performance (β = 0.019, t = 0.333, p = 0.739), thus H8 was rejected, but was found to indirectly influence academic performance through QOL (β = 0.077, t = 3.432, p < 0.001), supporting H9. As presented in Table 6, hypotheses number 10. 11, and 12 were also supported as regulation of emotions (as a dimension of EI) was found to have direct impact on disabled student QoL (β = 0.251, t = 6.002, p < 0.001) and academic performance (β = 0.444, t = 7.252, p < 0.001) and indirect impact on academic performance through QoL (β = 0.058, t = 3.519, p < 0.001). Finally, the direct impact of student QOL on academic performance showed a positive and significant path coefficient (β = 0.230, t = 4.367, p < 0.001), which supports H13. All the direct and indirect paths as shown in Table 6 are significant (except the path from use of emotion to academic performance) inferring a partial mediation effect.