Introduction
Although a bachelor’s degree has become a base requirement for most careers, many students who enroll in higher education programs struggle to complete them [70]. There are several factors that contribute to degree non-completion among students in the U.S. Broadly, the combined pressures of academic demands, financial burdens, and social commitments create barriers to students’ persistence in their degree programs [8, 20]. Illustrative of this point is the 6-year graduation rate for college and university students in the U.S: only 64% of students complete their degree/graduation within that time frame [70]. Although graduation rates have increased steadily over the past 15 years, progress for some historically underrepresented groups has stalled or even declined (e.g., graduation rates for Black and Latinx students [7].
Further degree completion is imperative to gainful and sustainable employment as competition in the job market increases. However, data across institutions of higher education in the U.S. shows that over 30% of students do not complete their degree in a six-year window after initial enrollment [21]. The amount of time it takes a student to complete a four-year college degree has increased over the same period [7]. The pattern of extending or delaying degree completion, and/or dropping out altogether, can lead to a cascade of effects for individual students, including negative effects on their self-esteem, financial deficits, and missed career opportunities [24, 77]. Higher education researchers have identified a host of factors, both at the institutional/structural level and the individual/psychological level that predict a student dropping out of a degree program. At the individual level, barriers to degree completion might include financial difficulties, difficulty adhering to academic routines and schedules, struggles with time management, and maladaptive stress coping strategies [51, 79]. In the current paper, we focus on the impact of one such psychological predictor of persistence in academic settings that is correlated with many of these individual level predictors: emotional intelligence.
Emotional intelligence (EI) broadly refers to individuals' ability to appraise (i.e., interpret), express (i.e., outwardly show situationally and culturally appropriate emotions that accurately convey feelings), and regulate (i.e., manage one’s emotional response in an emotion-provoking situation) emotions [36]. A large body of empirical evidence suggests emotional intelligence is a protective factor that supports academic performance and timely degree completion among university students [24, 31, 52]. Although, relationships between emotional intelligence and adaptative outcomes are well understood, our understanding of the mechanisms through which the construct contributes to academic success is somewhat limited [46]. As such, researchers have begun to investigate how emotional intelligence interacts with various cognitive, motivational, and interpersonal processes to influence academic outcomes [46]. However, we are aware of no studies that have directly examined the relationship between EI, motivational constructs identified in the expectancy-value model, and student retention. Thus, the current study was designed to determine whether the relationship between emotional intelligence and dropout is mediated by perceptions of expectancy and task-value.
Emotional intelligence
Decades of empirical work has demonstrated that academic success results from a complex interplay among interpersonal and contextual factors [29]. Students’ emotional intelligence has been shown to be a strong predictor of achievement emotions, student engagement, facets of academic motivation, and academic performance [33, 50, 62, 66, 81]. While researchers have developed numerous theoretical frameworks outlining the origins and outcomes of emotional intelligence, the ability and trait emotional intelligence models maintain a prominent position in the educational and psychological literature.
The ability model conceptualizes emotional intelligence as a set of inter-related cognitive abilities that support an individual’s ability to process and utilize affective information [14, 36]. Specifically, this model proposes that the cognitive abilities contributing to emotional intelligence can be categorized into four qualitatively distinct branches. The first branch, perceiving emotion, involves recognizing one's own emotions, identifying others' emotional states using verbal and non-verbal cues, and distinguishing between authentic and deceptive emotional displays. Branch two, facilitating thought using emotions, refers to generating emotions that enhance reasoning capabilities, abstract thinking, and interpersonal communication. The third branch, understanding emotion, involves understanding the antecedents and outcomes of specific emotions, understanding complex emotional experiences, and engaging in affective forecasting. The fourth branch, managing emotions, involves regulating one's own and others' emotions in a manner that supports goal-directed behavior [14, 35, 36]. Critically, the cognitive skills outlined in the ability model are believed to be hierarchical,skills positioned in the “lower” branches (e.g., perceiving emotions must be developed before an individual can engage in the complex reasoning characteristics of the “higher” branches (e.g., managing emotions; [23]).
The trait model conceptualizes emotional intelligence as a set of self-beliefs located at the lower levels of the personality hierarchy that influence how individuals engage with emotional information [43, 44]. Specifically, trait emotional intelligence theorists believe the construct primarily represents individuals’ confidence in their ability to perceive and regulate their own and others’ emotions and successfully navigate emotionally-charged situations [42, 43]. It is assumed these self-beliefs influence cognitive, motivational, and affective processes that are direct determinants of academic and mental health outcomes [34].
The ability and trait models emphasize the contributions of emotion perception, regulation and expression to emotional intelligence. However, slight variations in the conceptualization of the construct have important implications for measurement. For instance, trait theorists suggest that emotional self-perceptions are best measured through self and other reported measures, similar to other personality constructs. In contrast, ability theorists believe the abilities underlying emotional intelligence should be assessed using performance measures (similar to methods of assessing cognitive abilities; [35, 43]). While self-report and performance measures of emotional intelligence are differentially related to various outcomes in mental health, workforce, and academic domains [23, 31], research from the ability and trait traditions has shown that well-developed emotional intelligence is associated with adaptive educational outcomes. For example, students high in emotional intelligence report fewer negative achievement emotions (e.g., academic anxieties; [49, 65], higher levels of affective, cognitive, and social engagement [33, 63, 81], elevated academic motivation [6, 60], and exhibit increased academic performance (i.e., grades, test scores, GPA [31, 66]. Of particular relevance to the current investigation is prior research suggesting that emotional intelligence supports timely degree completion. For instance, Keefer and colleagues [24] asked first semester undergraduate students to complete a measure of trait emotional intelligence and tracked their academic progress over a 6-year period using university records. Using a combination of latent profile analysis and logistic regression, the researchers demonstrated that students high in emotional intelligence were more likely to persist and complete their program of study [24].
The benefits of the emotional intelligence within educational contexts are often attributed to its influence on coping potential. Empirical evidence indicates that students high in emotional intelligence are better at identifying and implementing adaptive coping strategies when faced with stress and academic difficulties than their peers with low emotional intelligence [30, 39, 66]. Thus, students with low emotional intelligence who struggle to regulate negative and/or deactivating achievement emotions are more susceptible to motivational and information processing issues within stressful learning situations, which can, in turn influence academic decision-making and ultimately academic performance [5, 65]. Although the links between emotional intelligence, coping, and academic success are well established within the literature [30, 45, 53, 59], additional mechanisms via which emotional intelligence contributes to adaptive academic outcomes remain unclear. As such, educational and psychological researchers have begun to explore how emotional intelligence influences aspects of academic motivation—a key determinant of academic performance and student retention.
Expectancy, task-value, and emotional intelligence
The expectancy-value model of achievement motivation proposes that academic choice, persistence, and performance are influenced by two interrelated factors—subjective task-value and expectancy [68, 75]. Subjective task-value refers to students’ valuation of a given academic domain or task, and is influenced by the perceived qualities and outcomes of academic activities [13]. Specifically, subjective task-value is enhanced when the task (1) aligns with or allows learners to express important aspects of their identity (i.e., attainment value), (2) is enjoyable (i.e., intrinsic value), and (3) is seen as useful for achieving future goals [13, 68, 74]. Additionally, potential negative consequences that result from engaging in an academic task, such as effort expenditure, negative emotional and psychological states, or missed opportunities, can reduce subjective task-value [13]. Expectancy refers to learners’ beliefs about their competence and ability to successfully complete academic tasks [76]. According to Eccles and colleagues, individuals are more likely to devote effort to and pursue academic goals if they believe they can successfully accomplish academic tasks (i.e., high expectancy) and if they value the outcomes associated with those activities (i.e., high subjective task-value).
Prior work has shown that emotional intelligence is associated with outcomes that are conceptually related to subjective task-value and expectancy. For instance, expectancy-value theory predicts that students’ academic behavior is influenced by the amount of enjoyment experienced during learning events (i.e., intrinsic value). Recent work focused on predictors of student engagement in higher education settings indicates that students high in emotional intelligence experience more positive achievement emotions (e.g., interest, enjoyment; [54, 62]) than their low emotional intelligence peers. Expectancy-value theory also suggests that motivation is reduced when students believe engaging in academic tasks will lead to negative psychological and emotional outcomes (i.e., high perceived cost). Emotional intelligence has consistently been shown to support adaptive coping and protect individuals from the experience of stress and negative affective states [57, 64, 82]. Finally, a small but growing body of literature suggests that emotional intelligence enhances perceptions of self-efficacy [11, 59, 67, 71], a construct that is conceptually similar to expectancy beliefs outlined in the expectancy value model. However, few studies have specifically examined how emotional intelligence interacts with subjective task-value and expectancy to influence student outcomes.
Current study
This study seeks to understand the relationship between emotional intelligence, subjective task-value, expectations for success, and student’s commitment to completing their university degree and persisting in their current academic program. Specifically, we investigated whether the relationship between emotional intelligence and dropout intention is mediated by constructs described in the expectancy-value theory of academic motivation. Based on our understanding of the literature, we developed the following hypotheses:
H1: Emotional intelligence will have a direct, negative effect on students’ dropout intention.
H2: The relationship between emotional intelligence and dropout intention will be mediated by intrinsic value.
H3: The relationship between emotional intelligence and dropout intention will be mediated by perceived cost.
H4: The relationship between emotional intelligence and dropout intention will be mediated by expectancy.
Methods
Participants
Participants (N = 337) were undergraduate and graduate students attending a public university in the Southern United States. Of these students, 85.89% identified as female (n = 286), 13.51% identified as male (n = 45), and 0.60% (n = 2) identified as transgender or nonbinary. A majority of the sample self-identified as Caucasian or White (66%, n = 223). The sample also consisted of students who identified as Hispanic or Latino (19.22%, n = 64), Black or African American (6.31%, n = 21), Asian or Pacific Islander (3.30%, n = 11), bi-racial (2.10%, n = 2.10) and Native American or American Indian (< 1%, n = 3). Four participants elected not to report their ethnic identity. The mean age of participants was 27.92 years (SD = 9.02).
Measures
Emotional intelligence
We assessed participants’ emotional intelligence using the 16-item Wong and Law Emotional Intelligence Scale (WLEIS; [28]). The WLEIS is designed to assess four unique dimensions of emotional intelligence, including Self-Emotions Appraisal (e.g., “I have a good understanding of my own emotions”), Others-Emotions Appraisal (e.g., “I am a good observer of others’ emotions”), Use of Emotion (e.g., “I am a self-motivating person”), and Regulation of Emotion (e.g., “I am quite capable of controlling my own emotions”). Participants reported their level of agreement with each statement using a 7-point Likert-type scale (1 = totally disagree, 7 = totally agree). Prior investigations have provided evidence supporting the multi-dimensional nature, internal consistency, predictive validity, and discriminant validity of the instrument [28]. Additionally, investigations have shown items can be used to create a global emotional intelligence score [73]. The results of reliability analysis indicated the overall instrument demonstrated an acceptable level of internal consistency when applied to our university sample (Cronbach’s α = 0.92, McDonald’s ω = 0.92).
Subjective task-value and expectancy beliefs
We assessed participants’ subjective task-value toward their academic major using an adapted version of a self-report instrument developed by Gaspard and colleagues (2017). Specifically, this instrument is designed to assess multiple dimensions of students’ value beliefs including Intrinsic Value (e.g., “I enjoy dealing with the topics in my major.”), Importance of Achievement (e.g., “Performing well in my major is important to me”), Personal Importance (e.g., “My major is very important to me personally.”), Utility for Daily Life (e.g., “What we learn in my major is directly applicable in everyday life.”), Utility for Job (e.g., “A good knowledge of my major will help me in my future job.”), Utility for School (e.g., “To be good at my major will help me in the remaining years at school.”), Social Utility (e.g., “Being well versed in my major will go down well with my classmates.”), Effort and Emotional Cost (e.g., “Doing activities related to my major is exhausting to me.”), and Opportunity Cost (e.g., “I have to give up other activities that I like to be successful at my major.”). Participants reported their overall level of agreement with each of the presented statements using a 4—point Likert-type scale (1 = strongly disagree, 4 = strongly agree).
Prior to data analysis, items for several subscales were averaged to create indices of higher-order task-value constructs. Specifically, Importance of Achievement and Personal Importance items were averaged to create an estimate of Attainment Value. Additionally, the Utility for Daily Life, Utility for Job, Utility for School, and Social Utility items were averaged to create an estimate of Utility Value. Effort and Emotional Cost and Opportunity Cost items were average to create an estimate of Cost. Reliability analysis results indicated that the Intrinsic Value (Cronbach’s α = 0.91, McDonald’s ω = 0.92), Attainment Value (Cronbach’s α = 0.86, McDonald’s ω = 0.89), Utility Value (Cronbach’s α = 0.88, McDonald’s ω = 0.89), and Cost (Cronbach’s α = 0.91, McDonald’s w = 0.91) subscales demonstrated acceptable internal consistency in the current investigation.
Expectancy
We measured students’ expectancy beliefs using a modified version of items created by Kosovich and colleagues [26]. Specifically, participants were presented with the following items: “I know I can learn the material in my major”; “I believe I can be successful in my major”; and “I am confident I can understand the material presented in my major courses.” Participants reported their level of agreement with each item using a using a 4—point Likert-type scale (1 = strongly disagree, 4 = strongly agree). The results of a reliability analysis indicated that the expectancy items demonstrated acceptable internal consistency (Cronbach’s α = 0.90, McDonald’s ω = 0.90).
Dropout intention
We measured the extent to which students were planning to leave their academic major or university using the 4-item Dropout Intention Scale developed by Dresel and Grassinger [12]. Sample items include: “I often think about changing my major or dropping out of university,” and, “The thought often crosses my mind that my current major is not for me.” Participants reported their agreement with each statement using a 4-point Likert-type scale (1 = strongly disagree, 4 = strongly agree). The results of a reliability analysis revealed that the Dropout Intention Scale exhibited acceptable internal consistency when applied to our university sample (Cronbach’s α = 0.81, McDonald’s ω = 0.82).
Procedures
Participants were recruited using a convenience sampling methodology. Specifically, participants completed the study materials through their involvement in a departmental research pool. The study was promoted on the departmental SONA website and all students who participated received course credit for completing the survey. Student participants provided informed consent and completed the survey materials using the Qualtrics Survey management platform. The self-report instruments (except for the demographic form which was completed last) were presented in a random order to reduce the potential for problematic order effects. The self-report instruments used in the current investigation were selected because of their established psychometric properties and conceptual alignment with the constructs of interest. The study protocol was reviewed and approved by the University of Texas at Tyler Institutional Review Board. The review by the local IRB ensured that the study procedures and protections for participants complied with U.S. Federal Law (Department of Health and Human Services Common rule).
Results
Harman’s single-factor test
It is possible that bias resulting from the use of a single method to collect data about the constructs of interest could influence the results of our statistical analyses (i.e., common method bias; [25]). Therefore, we used Harman’s single-factor test to determine if common method bias was present in the current investigation [47]. Specifically, all survey items associated with the constructs of interest were subjected to an exploratory factor analysis. The exploratory factor analysis used principal axis factoring to extract a single factor. We conducted the exploratory factor analysis using the JAMOVI statistical package [61]. Prior research suggests that common method bias is present if a single factor accounts for more than 50% of the variance in the observed data [16]. The results of the exploratory factor analysis indicated that the extracted factor accounted for approximately 27.43% of the variance in the collected data. This finding suggests that common method bias was not present in the current investigation.
Descriptive statistics and correlational analyses
We conducted a series of correlational analyses to assess the relationship between emotional intelligence, task-value elements, expectancy, and dropout intention. Our results indicated that emotional intelligence was positively associated with intrinsic value (r = 0.24, p < 0.001), attainment value (r = 0.27, p < 0.001), utility value (r = 0.27, p < 0.001), and expectancy (r = 0.34, p < 0.001). Further, emotional intelligence was negatively associated with perceived cost (r = − 0.12, p = 0.01) and self-reported dropout intention (r = − 0.22, p < 0.001). The results of the correlational analyses also revealed that intrinsic (r = − 0.52, p < 0.001), attainment (r = − 0.54, p < 0.001), and utility value (r = − 0.34, p < 0.001) as well as expectancy (r = − 0.47, p < 0.001), were negatively associated with dropout intention. Finally, our findings indicated that perceived cost was positively associated with dropout intention (r = 0.42, p < 0.001). Descriptive statistics and correlational analysis results exploring the relationship among the primary constructs of interest are presented in Table 1.
Table 1. Pearson product moment correlation coefficients and descriptive statistics for Emotional Intelligence, Intrinsic Value, Attainment Value, Utility Value, Cost, Expectancy, and Dropout Intention
Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
1. Emotional Intelligence | – | ||||||
2. Intrinsic value | 0.24* | – | |||||
3. Attainment value | 0.27* | 0.64* | – | ||||
4. Utility value | 0.27* | 0.56* | 0.58* | – | |||
5. Cost | − 0.12* | − 0.40* | − 0.36* | − 0.24* | – | ||
6. Expectancy | 0.34* | 0.54* | 0.64* | 0.48* | − 0.30* | – | |
7. Dropout intention | − 0.22* | − 0.52* | − 0.54* | − 0.34* | 0.42* | − .47* | – |
Mean | 5.22 | 3.41 | 3.61 | 3.30 | 2.09 | 3.56 | 1.59 |
SD | 0.97 | 0.52 | 0.44 | 0.44 | 0.70 | 0.53 | 1.40 |
Skewness | − 0.32 | − 0.36 | − 1.08 | − 0.27 | 0.15 | − 0.93 | 1.04 |
Kurtosis | − 0.39 | − 0.45 | 0.23 | − 0.26 | − 0.60 | 0.36 | 1.07 |
*p < 0.05
Mediation analysis
We used mediation analysis to determine whether the relationship between emotional intelligence and dropout intentions was mediated by dimensions of subjective task-value and students’ expectancy beliefs. The mediation analyses were conducted using the open-source JAMOVI statistical package [61] and the advanced mediation models (jAMM) module [17].
Total and direct effects
Our review of the mediation findings began with total and direct effect estimates. The total effect estimate revealed a negative relationship between emotional intelligence and dropout intentions (β = − 0.23, p < 0.001) suggesting that students’ emotional competencies influence their decision to continue study in their academic major or at their university. Further, direct effect estimates indicated that intrinsic value (β = − 0.23, p < 0.001), attainment value (β = − 0.30, p < 0.001), and cost (β = 0.23, p < 0.001) exerted a direct influence of students’ dropout intention. The direct effect estimates also revealed that emotional intelligence shared a statistically significant relationship with all expectancy-value components. Specifically, emotional intelligence was shown to enhance perceptions of intrinsic value (β = 0.25, p < 0.001), attainment value (β = 0.27, p < 0.001), utility value (β = 0.27, p < 0.001), and expectancy (β = 0.34, p < 0.001) and reduce perceived cost (β = − 0.12, p = 0.03). Interestingly, the direct effect estimates revealed statistically non-significant relationships between emotional intelligence and dropout intentions (β = − 0.05, p = 0.25), utility value and dropout intention (β = 0.07, p = 0.26), and expectancy and dropout intention (β = − 0.14, p = 0.05).
Indirect effects
Next, we reviewed indirect effect estimates to determine if emotional intelligence influences dropout intention through subjective task-value elements and expectancy. The indirect effect estimates indicated that the relationship between emotional intelligence and dropout intentions was mediated by intrinsic (β = − 0.05, p = 0.006) and attainment value (β = − 0.08, p = 0.001). Further, our results indicated that the indirect effects of emotional intelligence on dropout intentions through cost (β = − 0.02, p = 0.06) and expectancy (β = − 0.04, p = 0.06) were not statistically significant. However, it is important to note that the indirect effects involving cost and expectancy approached statistical significance. Finally, the results of the analysis revealed the indirect effect involving utility value (β = 0.02, p = 0.28) was not statistically significant. The results of the mediation analyses are presented in Table 2. Further, a graphical representation of the mediation results is presented in Fig. 1.
Table 2. Specific indirect, direct, and total effects on dropout intention
95% C.I | |||||||
|---|---|---|---|---|---|---|---|
Effect | b | SE | Lower | Upper | β | p | |
Indirect | Emotional Intelligence → Intrinsic Value → Dropout Intention | − 0.03 | 0.01 | − 0.06 | − 0.01 | − 0.05 | 0.007 |
Emotional Intelligence → Attainment Value → Dropout Intention | − 0.04 | 0.01 | − 0.07 | − 0.02 | − .08 | 0.001 | |
Emotional Intelligence → Utility Value → Dropout Intention | 0.01 | 0.01 | − 0.01 | 0.03 | 0.02 | 0.28 | |
Emotional Intelligence → Cost → Dropout Intention | − 0.01 | 0.00 | − 0.03 | − 0.00 | − .02 | 0.05 | |
Emotional Intelligence → Expectancy → Dropout Intention | − 0.02 | 0.01 | − 0.05 | 0.00 | − .04 | 0.06 | |
Component | Emotional Intelligence → Intrinsic Value | 0.13 | 0.03 | 0.07 | 0.20 | 0.25 | < 0.001 |
Intrinsic Value → Dropout Intention | − 0.24 | 0.06 | − 0.37 | − 0.12 | − .23 | < 0.001 | |
Emotional Intelligence → Attainment Value | 0.12 | 0.02 | 0.07 | 0.17 | 0.27 | < 0.001 | |
Attainment Value → Dropout Intention | − 0.36 | 0.10 | − 0.56 | − 0.18 | − 0.30 | < 0.001 | |
Emotional Intelligence → Utility Value | 0.12 | 0.02 | 0.07 | 0.17 | 0.27 | < 0.001 | |
Utility Value → Dropout Intention | 0.09 | 0.08 | − 0.08 | 0.25 | 0.07 | 0.26 | |
Emotional Intelligence → Cost | − 0.09 | 0.04 | − 0.17 | − 0.00 | − 0.12 | 0.03 | |
Cost → Dropout Intention | 0.18 | 0.03 | 0.10 | 0.25 | 0.23 | < 0.001 | |
Emotional Intelligence → Expectancy | 0.18 | 0.03 | 0.12 | 0.24 | 0.34 | < 0.001 | |
Expectancy → Dropout Intention | − 0.14 | 0.07 | − 0.28 | 0.00 | − 0.14 | 0.05 | |
Direct Effect | Emotional Intelligence → Dropout Intention | − 0.03 | 0.02 | − 0.09 | 0.01 | − 0.05 | 0.25 |
Total Effect | Emotional Intelligence → Dropout Intention | − 0.14 | 0.03 | − 0.20 | − 0.07 | − 0.23 | < 0.001 |
Confidence intervals were computed using the percentile bootstrap method (n = 1000). Beta values are completely standardized effect sizes
Fig. 1 [Images not available. See PDF.]
Mediation Analysis Results; Note. Confidence intervals were computed using the percentile bootstrap method (n = 1000). Beta values are completely standardized effect sizes. *p < 0.05, nsp ≥ 0.05
Discussion
This study used mediation techniques to determine whether emotional intelligence influences dropout intention through the motivational outcomes outlined in expectancy-value theory. Contrary to our expectations and Hypothesis 1, the results of this study showed that there was not a direct relationship between emotional intelligence and students’ intention to leave their academic major or university. This result was unexpected given prior research suggesting that learners’ emotional intelligence is an important predictor of student degree completion [24]. However, we would like to emphasize that our results do not indicate that students’ emotional intelligence was unrelated to retention-related decisions. Instead, the absence of a direct effect, paired with the results presented below, provides support for the assertation that the effects of higher-order dispositional constructs—such as emotional intelligence—are transmitted through mediating processes (e.g., self-regulation, motivation) that have a direct relationship with academic behaviors and outcomes [34].
In support of Hypothesis 2, we found that emotional intelligence can decrease dropout intentions by fostering students’ sense of intrinsic value towards their academic major. Decades of research have demonstrated that individuals high in emotional intelligence, when compared to their peers with lower levels of the construct, possess a variety of coping responses and are better able to implement effective coping strategies when confronted with stressors [15, 37, 59]. Thus, it is no surprise that those higher in emotional intelligence are better able to navigate everyday academic challenges and sources of stress, and thus maximize positive emotions. Further, we would like to highlight that this mediating pathway further solidifies the role of positive emotions, such as enjoyment, as key predictors of students’ retention decisions [56, 78].
We further predicted that the relationship between emotional intelligence and students’ dropout intentions would be mediated by perceived cost (Hypothesis 3) and expectancy (Hypothesis 4). Our analyses indicated that these particular mediation pathways were not statistically significant. However, it is important to note that the mediating effects involving cost and expectancy approached statistical significance and were in the expected direction. Thus, this pattern of results is consistent with the previous literature noting that elevated emotional intelligence is associated with the implementation of adaptive coping and emotion regulation strategies which buffers individuals from negative emotions and stress within and beyond academic environments [29, 69]. Further, this pattern of results is consistent with a growing body of empirical literature suggesting that emotional intelligence enhances efficacy judgements [1, 10, 59, 67]. As noted in the literature, emotional experience is predicted to directly influence the establishment and maintenance of efficacy beliefs. Specifically, elevated physiological arousal and negative emotions (e.g., stress, anxiety) can prompt feelings of self-doubt, which undermine efficacy beliefs [4]. Thus, it is possible that the association between emotional intelligence and expectancy noted in this examination and other published works is tied to the fact that emotional intelligence enhances emotion regulation and buffers individuals from the perception of stress.
We made no specific prediction regarding the association between emotional intelligence, attainment value and dropout intention, but we believe it is important to address the significant mediation effect detected in our analysis. Specifically, our mediation analysis indicated that emotional intelligence increases attainment value, which in turn reduces dropout intention. Expectancy-value theory proposes that attainment value is closely associated with identity and self-expression. Specifically, individuals are more likely to value activities that allow them to reinforce important aspects of their identity [13]. Unsurprisingly then, attainment value is often elevated among those who have undergone sufficient identity exploration and constructed a well-developed identity related to the relevant domain [9, 41]. Interestingly, a growing body of literature has demonstrated that emotional intelligence is associated with identity development. For instance, studies by Seaton and Beaumont [55] and Maher and colleagues [32] show that those high in emotional intelligence are more likely to develop adaptive identity characteristics and identity processing styles. Although the mechanisms through which emotional intelligence influences identity construction have not been explored in depth, it is possible that emotional intelligence allows individuals to better navigate the complex emotional and interpersonal challenges associated with adaptive identity development and construction [22, 38]. Therefore, it is possible that student with high emotional intelligence in our sample possessed a more robust identity related to academics and their major which translated into elevated attainment value.
Practical implications
We believe the results of the present study have practical implications for efforts aimed at improving student persistence towards graduation in institutions of higher education. Our results suggest that emotional intelligence can mitigate dropout-focused thoughts by enhancing the perceived value of academics and expectancy beliefs. Thus, universities could support student retention through structured efforts to bolster students’ emotional intelligence. For instance, prior research has shown that interventions providing explicit instruction regarding the characteristics of emotional intelligence and effective emotion regulation strategies can enhance emotional intelligence in K-12 and university populations [19, 27, 48, 80]. In line with recommendations by leaders in the field [40], we believe potential barriers to the implementation of structured emotional intelligence support programs could be minimized by leveraging existing institutional mechanisms. Emotional intelligence training, for example, could be incorporated into services provided by student counseling centers, learning support programs, and student success departments. Additionally, the success of online emotional intelligence intervention efforts [3, 18] suggests universities could have success offering students access to online emotional intelligence training modules that could be completed at their convenience.
Additionally, the negative relationship between many of the expectancy-value components and dropout intention noted in this investigation can be used to inform student success efforts. For example, a meta-analysis evaluating the effectiveness of various student success initiatives in higher education indicated that programs featuring one-on-one mentoring, especially direct faculty-student mentoring, are the most effective at boosting persistence and graduation rates [58]. These types of interventions have been more effective than more punitive measures, such as academic probation. It is possible that the increased efficacy of these mentoring programs is due to the links that they create between students and a professional in their chosen field (i.e., a faculty member) which has the effect of boosting students’ understanding of the benefits of their degree, its practicality, and worth and by extension motivational constructs outlined in expectancy-value theory.
Limitations
The current study has several limitations that are worthy of additional discussion. First, it is important to note that this investigation relied on cross-sectional data to determine the relationship between emotional intelligence, task-value, expectancy, and drop-out intention. As such, it is not possible to make conclusive statements regarding potential causal relationships between variables of interest. Therefore, we encourage researchers to replicate our findings using longitudinal designs to isolate the contribution of emotional intelligence, task-value, and expectancy beliefs on subsequent drop-out intention and behavior. Second, our study relied on self-reported dropout intention to gain insight into student’s retention decisions. Although behavioral intentions have been shown to be robust predictors of future outcomes [2, 63, 72], we believe is important for researchers to replicate these findings using institutional data sources. Also, our study relied on survey methodology to assess the primary constructs of interest which increases the risk of common method bias. Although Harmans single factor test indicated common method bias was not an issue in the current examination, we believe it is important for others to replicate our findings while taking explicit steps to limit common method bias. Finally, our study was conducted at a single university located in the southern United States. The unique demographic characteristics of this sample might influence the extent to which our findings can be generalized to other institutions of higher education.
Author contributions
CLT conceived the study and was responsible for study design, securing ethical approval, and data collection. CLT and KM performed the statistical analyses and interpreted the primary findings. CLT, KM, and ARH drafted the manuscript. CLT, KM, and ARH read and approved the final version of the manuscript.
Funding
The study was not funded.
Data availability
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
Declarations
Ethics approval and consent to participate
All participants provided informed consent before completing the survey materials. Further, the study materials and procedure were approved by the University of Texas at Tyler Institutional Review Board. The review by the local IRB ensured that the study procedures and protections for participants complied with U.S. Federal Law (Department of Health and Human Services Common rule).
Competing interests
The authors declare no competing interests.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Abstract
Timely degree completion is a topic of interest for numerous stakeholders, including university officials, students, and parents. Unfortunately, a significant number of students encounter challenges within university settings that postpone degree completion or contribute to academic dropout. The available literature suggests that emotional intelligence functions as a protective factor that promotes academic resilience and decreases the likelihood of academic dropout. However, the mechanisms by which emotional intelligence supports degree completion are not fully understood. Therefore, the current study was designed to investigate if the relationship between emotional intelligence and dropout intentions is mediated by task-value and expectancy. University students (N = 337; 66.97% Caucasian/White; 85.89% female; Mean Age = 27.92) completed the Wong and Law Emotional Intelligence scale, self-report measures of task-value (i.e., attainment, intrinsic, utility, & cost) and expectancy, and the Dropout Intentions scale. Using mediation analysis, we determined there was no direct relationship between emotional intelligence and dropout intention. However, indirect effects revealed the relationship between emotional intelligence and dropout intention is mediated by intrinsic and attainment value. These results offer insights into how emotional intelligence may contribute to academic retention and have implications for interventions and support services designed to promote on-time degree completion.
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Details
; Maple, Kaevyn L. 2
; Hayes, Amy Roberson 3
1 The University of Texas at Tyler, School of Education, Tyler, USA (GRID:grid.267327.5) (ISNI:0000 0001 0626 4654)
2 Saint Louis University, Department of Psychology, St Louis, USA (GRID:grid.262962.b) (ISNI:0000 0004 1936 9342)
3 The University of Texas at Tyler, Department of Psychology and Counseling, Tyler, USA (GRID:grid.267327.5) (ISNI:0000 0001 0626 4654)