Next Article in Journal
Assessing Student Teachers’ Motivation and Learning Strategies in Digital Inquiry-Based Learning
Previous Article in Journal
Designing Dialogic Peer Feedback in Collaborative Learning: The Role of Thinq Tank
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Education Sciences
Volume 14
Issue 11
10.3390/educsci14111232
education-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Impact Mechanism of Negative Academic Emotions on Academic Procrastination: The Mediating and Moderating Roles of Self-Efficacy and Goal Orientation

by
Bowen Chen
,
Hongfeng Zhang
* and
Sigan Li
*
Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao 999078, China
*
Authors to whom correspondence should be addressed.
Educ. Sci. 2024, 14(11), 1232; https://doi.org/10.3390/educsci14111232
Submission received: 2 October 2024 / Revised: 5 November 2024 / Accepted: 8 November 2024 / Published: 11 November 2024
Browse Figures
Versions Notes

Abstract

:
Academic procrastination is a widespread phenomenon among college students, significantly affecting their academic performance and mental health. Although previous studies have suggested a relationship between negative academic emotions and academic procrastination, the underlying mechanisms of this relationship remain insufficiently explored. Based on theoretical analysis and a review of the literature, this study utilizes structural equation modeling to examine the effects of negative academic emotions, self-efficacy, and goal orientation on academic procrastination. The results indicate a significant positive correlation between negative emotions and academic procrastination. Furthermore, self-efficacy fully mediates the relationship between negative academic emotions and academic procrastination, while goal orientation plays a moderating role in this relationship. This study reveals the intricate relationships between negative academic emotions and academic procrastination among Chinese college students, emphasizing the importance of enhancing self-efficacy and goal orientation to prevent procrastination. It provides theoretical and empirical support for improving learning efficiency and academic achievement, as well as for designing interventions to address academic procrastination.

1. Introduction

Procrastination, as a prevalent behavioral phenomenon in the learning process, has increasingly garnered the attention of researchers. Numerous studies have demonstrated that procrastination, particularly among university students, and its accompanying negative effects are becoming more pronounced. Issues such as declining academic performance, decreased self-confidence and self-esteem, impaired mental health, and lower life satisfaction resulting from the delay or poor-quality completion of academic tasks have raised widespread concern [1,2,3,4]. Many university students, in their pursuit of academic success, have experienced the challenges of procrastination [5]. This issue is even more prominent among Chinese university students, where cultural differences, emotional pressure, high social expectations, and stringent self-demands intensify the interaction between procrastination and other psychological factors [6,7]. Procrastination has become one of the most common learning obstacles preventing Chinese university students from realizing their potential [3,4,8]. Both students affected by procrastination and educators striving to help them have recognized that procrastination is a major barrier to achieving academic potential. However, despite procrastination contradicting individuals’ intentions, it stubbornly persists in practice [9]. Many procrastinators wish to change their behavior but often face the frustration of being unable to do so, limited effort, or ineffective attempts, ultimately resigning to the status quo. The core issue of procrastination lies in the gap between intention and action, which is fundamentally a failure of self-regulation. Without a comprehensive understanding of the mechanisms behind procrastination, mere desire or determination to change is unlikely to solve the problem effectively [10]. Therefore, in order to effectively address procrastination and mitigate its negative effects, it is essential to explore the deeper causes and psychological mechanisms behind procrastination.
In studies on the mechanisms influencing academic procrastination, scholars have explored a variety of factors, such as negative emotions [11,12,13], self-efficacy [11,14,15], goal orientation [15], self-regulation ability [16], cognitive beliefs [17], personality traits [18], as well as sociocultural environment [19], online learning environments [20], type of public school [21], and parenting styles [22,23]. Although these studies have demonstrated correlations between various factors and procrastination, they primarily focus on the direct relationships between independent and dependent variables. There remains a need to further explore the potential interactions between these factors. The sheer number of proven influencing factors indirectly suggests the complexity of the psychological mechanisms behind procrastination. The occurrence of procrastination likely involves multiple elements, with several factors interacting and potentially influencing each other. Simply discussing the isolated effects of individual factors is insufficient in regards to providing a comprehensive explanation of procrastination. Therefore, it is necessary to construct an integrative psychological model based on theoretical and empirical support, combining these well-established psychological factors into a scientifically valid framework to reveal the interactive mechanisms that influence academic procrastination among Chinese university students.
In academic contexts, Chinese university students often face considerable academic pressure, challenging content, insufficient interest in learning, and fast-paced study schedules, all of which increase the likelihood of experiencing negative emotions [24]. Negative academic emotions can disrupt students’ psychological balance, affecting their learning state, reducing their confidence and expectations for achieving goals, and ultimately weakening their motivation to initiate goal-oriented behavior. As a result, procrastination often emerges as a means of avoiding undesirable tasks, leading from negative academic emotions to a diminished sense of academic goals. This effect may be further exacerbated by the common traits of low self-efficacy and unclear goals in university students. In discussions of learning motivation, self-efficacy and goal orientation are considered crucial concepts. According to Bandura’s theory, learners’ confidence in their abilities directly influences their behavioral choices, making students with high self-efficacy more likely to actively face academic challenges and thus reduce procrastination [25,26,27]. Self-efficacy refers to an individual’s belief in their ability to successfully complete a task, while goal orientation reflects the degree to which individuals perceive and value their goals. Both self-efficacy and goal orientation have been shown to be negatively correlated with procrastination. Meanwhile, Gross’s emotion regulation theory highlights the importance of effective emotion management in the learning process, positing that learners can enhance their self-efficacy by managing negative emotions, thereby strengthening their motivation to learn [28,29]. Jacquelynne Eccles’s expectancy–value theory provides a perspective for understanding learning motivation, emphasizing how individuals’ expectations of success and perceptions of task value drive learning behaviors [23,30]. When individuals have high self-esteem regarding their ability to complete a task and perceive a high value in doing so, their willingness to take action increases and the likelihood of procrastination decreases [31,32]. In summary, these theories emphasize the importance of emotions, self-efficacy, and a sense of purpose in behavioral choice and execution, jointly providing a new integrated framework for understanding procrastination. It can thus be inferred that, when individuals experience predominantly negative emotions regarding a task, coupled with low self-efficacy and sense of purpose, their motivation is weakened, increasing the likelihood of procrastination.
To address the growing influence of negative emotions, particularly in learning contexts, this study is grounded in the expectancy–value theory, focusing on three key factors: negative emotions perceived before goal execution, goal orientation, and self-efficacy related to the task execution process [33,34]. By employing a structural equation model incorporating both factor analysis and path analysis, this study seeks to examine the interactions of these psychological factors within the context of Chinese university students, providing a more comprehensive theoretical and empirical foundation for understanding academic procrastination [35].

2. Hypotheses and Theoretical Model

2.1. The Relationship Between Negative Academic Emotions and Academic Procrastination

Emotions are psychological states that arise in response to stimuli and can encompass a wide range of experiences, including joy, anger, sadness, and happiness. Emotions play a crucial role in individuals’ cognition, decision-making, and social activities. During the learning process, students may experience both positive and negative academic emotions. While positive emotions facilitate the learning process, negative academic emotions negatively impact students’ learning attitudes and behaviors. According to the emotion regulation theory, when individuals experience discomfort or stress due to emotions, they often adopt various strategies to regulate their emotional states. Procrastination is considered one of the most common regulation strategies [36,37]. In other words, individuals may temporarily avoid tasks by diverting their attention to alleviate the negative emotions caused by academic tasks, thereby restoring emotional balance and comfort.
Several empirical studies have identified a close relationship between the experience of negative emotions during the learning process and procrastination. For example, Rothblum compared the emotional, cognitive, and behavioral differences between high and low procrastinators and found that high procrastinators were more likely to report increased states of anxiety and anxiety-related physical symptoms [38]. Baumeister argued that emotional distress is a key factor in triggering avoidant behaviors and risky actions [39]. Tice explored the relationship between emotional regulation failure and self-control failures in other areas, such as procrastination, alcoholism, and gambling [40]. Fee examined the relationship between chronic procrastination and feelings of shame and guilt, concluding that emotions play a vital role in understanding the complex dynamics of chronic procrastination [41]. Ariati conducted an experimental study showing that a five-week emotional training program effectively reduced procrastination [42]. Steel’s meta-analysis of 691 potential factors contributing to procrastination identified task aversion, self-efficacy, task responsibility, and attentional distraction as major correlates [43]. Onwuegbuzie attributed the tendency of most undergraduate and graduate students to delay academic tasks to their fear and aversion to academic failure [44].
Based on the findings of these studies, researchers generally agree that there is a significant positive correlation between negative emotions and academic procrastination. When students experience stress, frustration, or other negative emotions during the learning process, they are more likely to temporarily avoid academic tasks, leading to procrastination. Therefore, this study proposes the following hypothesis:
H1: 
Negative academic emotions positively predict academic procrastination.

2.2. The Mediating Role of Self-Efficacy in the Relationship Between Negative Emotions and Academic Procrastination

Self-efficacy, a key concept in Bandura’s learning psychology theory, is defined as an individual’s belief in their ability to successfully complete a task. Bandura posits that self-efficacy is a core mediating factor in individuals’ behavioral mechanisms, influencing various behaviors in a manner similar to motivation [32,45]. It is important to note that self-efficacy differs from general confidence; it is a future-oriented and context-specific construct that emphasizes an individual’s belief in their abilities before and during task executions [46,47]. In this study, students’ judgments about their self-efficacy are primarily derived from four sources. These factors may vary in their influence on self-efficacy, but, collectively, they shape an individual’s overall perception of their capabilities.
  • Mastery Experiences: This refers to direct feedback from personal successes or failures. Positive mastery experiences strengthen self-efficacy, enhancing students’ ability to cope with negative emotions and reducing the likelihood of procrastination. Conversely, failure experiences may lower self-efficacy, making students more prone to using procrastination as a coping strategy [48,49,50].
  • Vicarious Experiences: Observing others, especially peers with similar backgrounds, successfully complete tasks can positively impact students’ self-efficacy. When facing negative emotions, witnessing others’ success can inspire students to believe that they also can overcome challenges, thus reducing the tendency to procrastinate [51,52].
  • Verbal Persuasion: Encouragement and support from others, particularly positive feedback from teachers and peers, significantly boost students’ self-efficacy. In times of academic pressure and negative emotions, external positive persuasion can help students build confidence, motivating them to take action and decrease procrastination [48,53,54].
  • Physiological States: Students’ physiological responses (e.g., tension, anxiety, or excitement) directly affect their self-efficacy. Positive physiological states are generally associated with higher self-efficacy, while negative states may intensify doubts about one’s abilities. Regulating physiological states during learning can be a vital strategy for enhancing self-efficacy, helping students better manage negative emotions and reduce procrastination [55,56].
Furthermore, these factors not only influence students’ self-efficacy but also serve as moderators in the relationship between negative emotions and academic procrastination. When negative emotional events occur, self-efficacy is often directly impacted. On one hand, negative emotional events can disrupt an individual’s physiological and psychological equilibrium, occupying limited cognitive and psychological resources, thus impairing concentration and reducing their capacity to process and make decisions on complex information. This, in turn, diminishes self-efficacy. On the other hand, negative emotional events increase the individual’s perception of uncertainty and lack of control over tasks, undermining their confidence and belief in their abilities, which further weakens their self-efficacy.
Regarding the relationship between self-efficacy, negative emotions, and procrastination, social cognitive theory suggests that behavior is regulated not only by external environmental factors but also by beliefs and expectations. Social cognitive theory emphasizes the reciprocal interaction between cognition, emotions, and environmental factors [57,58]. Building on this, self-efficacy theory highlights the importance of an individual’s confidence and belief in their abilities when it comes to behavior selection and execution. Self-efficacy theory stresses that belief in one’s abilities plays a central role in behavioral choices, goal commitment, engagement levels, and personal achievement [32,59]. Both theories jointly assert that self-efficacy has a significant influence on behavior regulation. Individuals are more likely to actively engage in tasks where they have high efficacy beliefs, whereas they tend to adopt passive and avoidant behaviors in tasks where their efficacy beliefs are lower.
Moreover, numerous studies have demonstrated the negative correlations between negative emotions and self-efficacy, as well as between self-efficacy and procrastination. For instance, Ferrari found that, while procrastination frequency among university students was not significantly related to procrastination type or locus of control, it was negatively correlated with self-efficacy, with task aversion being a key motivating factor [14]. Klassen, in examining the relationships between self-regulation, self-esteem, academic self-efficacy, and academic procrastination, found that self-efficacy was the most predictive variable of procrastination tendencies [2]. In a study of 141 university students, Haycock evaluated the effects of self-efficacy and anxiety on procrastination and found significant correlations between self-efficacy and anxiety [11]. Cerino, through a hierarchical regression model, demonstrated a significant negative correlation between academic procrastination and three types of intrinsic motivation, one type of extrinsic academic motivation, and self-efficacy [60].
Based on the findings from these studies, self-efficacy is influenced by negative emotions and, in turn, affects the occurrence and degree of procrastination. Negative emotions negatively impact an individual’s self-efficacy, and self-efficacy negatively predicts the likelihood of procrastination. Therefore, it is hypothesized that self-efficacy may mediate the relationship between negative emotions and procrastination. Accordingly, in addition to analyzing the relationship between negative academic emotions and procrastination, this study proposes the following hypotheses:
H2: 
Self-efficacy negatively influences academic procrastination.
H3: 
Negative academic emotions negatively influence self-efficacy.
H4: 
Self-efficacy mediates the relationship between negative academic emotions and academic procrastination.

2.3. The Moderating Role of Goal Orientation in the Relationship Between Negative Emotions and Academic Procrastination

Goals refer to an individual’s expected outcomes for a given activity, while goal orientation refers to the degree of personal recognition and perception of these goals. This concept includes the alignment between personal values and goals, as well as the individual’s clarity regarding the path to achieving these goals [31]. According to Edwin Locke’s goal-setting theory, goals themselves are inherently motivating, and any external stimulus affects behavioral motivation and performance through an individual’s perception of the goal [61,62,63]. It can be inferred that, when faced with the same task, individuals with a high level of goal orientation—due to their strong perception of the goal’s feasibility and importance—are more likely to develop a clear sense of direction and purpose. Consequently, they exhibit a higher level of willingness and engagement in the task. Atkinson’s achievement motivation theory posits that motivation (Ta) = the tendency to pursue success (Ts)—the tendency to avoid failure (Taf) (Anderman, 2020; Brunstein et al., 2018). Therefore, when individuals with high goal orientation experience negative emotions that might lead to procrastination, their strong drive to achieve success can offset the avoidant tendencies triggered by negative emotions, helping them maintain the motivation to pursue their goals and reducing the likelihood of procrastination. In contrast, individuals with weaker goal orientation may lack the drive to counteract the negative emotions, leading them to procrastinate or abandon their goal-directed tasks.
In addition, empirical research supports the important role of goal orientation in reducing the tendency to procrastinate academically. For instance, Muñoz-Olano’s study evaluated whether using the SMART method to clarify goals could reduce academic procrastination, with the results confirming that clearer goals effectively decrease procrastination [64]. Gustavson examined the relationship between goal management and academic procrastination through strategy training experiments and provided new evidence while also noting the need for further research on effective intervention measures [10]. Theobald, using independent intensive longitudinal data, tested the effects of ability beliefs, value beliefs, and goal failure on procrastination. The study found that higher ability and value beliefs were associated with greater academic achievement, while procrastination was related to lower goal attainment [65]. Hsueh explored whether group size influences goal-setting and academic procrastination, with field research and interviews showing that groups of five had a greater impact on goal-setting and lower rates of procrastination compared to groups of two. Kaftan conducted a 14-week longitudinal study investigating whether goal focus (the degree of attention on goal achievement or task outcome) is related to procrastination. The results indicated that goal focus had a stronger influence on procrastination compared to process focus [66]. These studies collectively demonstrate that goal orientation helps individuals protect valuable goals and reduce the likelihood of procrastination.
Based on the above discussion, the following hypothesis is proposed:
H5: 
Goal orientation moderates the relationship between negative academic emotions and procrastination.
In summary, and based on the analysis and discussion of the relevant literature and theories, this study will use negative academic emotions (NAEs) as the independent variable, academic procrastination (AP) as the dependent variable, self-efficacy (SE) as the mediating variable, and goal orientation (SOP) as the moderating variable to investigate the factors influencing academic procrastination. The preliminary structural equation model is shown below (Figure 1).

3. Research Methods and Tools

3.1. Sampling and Data Collection

In this study, a stratified random sampling method was employed through a questionnaire survey. The overall population was divided into several strata based on key variables such as gender and grade level. Then, random samples were drawn from each stratum to ensure both diversity and representativeness in support of subsequent statistical analyses. The use of stratified random sampling was chosen to minimize sampling errors and enhance the external validity of the results. In this study, the population was stratified by gender (male = 102, female = 157) and academic year (freshman = 36, sophomore = 69, junior = 80, senior = 54, graduate = 20), with samples randomly being selected from each stratum to ensure sufficient sample sizes in each group to support subsequent statistical analyses.
The respondents for this study were university students from higher education institutions in the Guangdong–Hong Kong–Macao Greater Bay Area, China. The demographic information includes region (Hong Kong Special Administrative Region: 20 students, Macau Special Administrative Region: 25 students, and Guangdong Province: Guangzhou: 35, Shenzhen: 33, Zhuhai: 31, Foshan: 25, Huizhou: 20, Dongguan: 18, Zhongshan: 25, Jiangmen: 14, Zhaoqing: 13), gender (male = 102, female = 157), and grade level (first-year undergraduate = 36, second-year undergraduate = 69, third-year undergraduate = 80, fourth-year undergraduate = 54, graduate students = 20).
The questionnaire was distributed and collected online. A total of 329 questionnaires were received, of which 70 were deemed invalid, resulting in 259 valid questionnaires with a response rate of 78.7%. The collection of stratified random sampling questionnaires was primarily conducted by generating different questionnaire links based on the strata (e.g., gender or grade level) and distributing them to targeted groups. During the data collection process, the sample composition was continuously monitored to ensure that the sample size for each stratum met the predetermined proportion. If the sample size for a particular stratum was insufficient, additional sampling from that stratum was conducted and random sampling was used to maintain representativeness. For instance, separate questionnaire links were created based on gender or grade level and distributed to specific populations.
The criteria for excluding invalid questionnaires were as follows:
(1)
Incomplete responses: Questionnaires with more than 20% unanswered items were considered invalid.
(2)
Inconsistent answers: If contradictory responses were selected for similar items, the questionnaire was deemed invalid.
(3)
Duplicate submissions: If the same respondent submitted multiple questionnaires, only the first submission was retained, and subsequent submissions were treated as invalid.
Additionally, in the design and implementation of the questionnaire, this study carefully considered cultural adaptability by referencing literature that was relevant to the cultural context [20,35,54,67,68]. Before the official distribution, a pilot test was conducted with a small group of students from various academic years and majors. Based on the initial feedback, certain items were revised in wording and sequence to better align with the Chinese cultural context, ensuring suitability for Chinese students. The questionnaire has undergone reliability and validity testing to confirm the reliability and effectiveness of the scales.

3.2. Measurements

3.2.1. Negative Academic Emotions Scale

The study utilized the Negative Academic Emotions Scale developed by Liu et al. [69], which consists of 6 items covering two dimensions: emotional responses and somatic responses. Responses were measured using a 5-point Likert scale. The choice of this scale was due to its well-structured design and its wide application in assessing students’ emotional responses in academic settings [68,70,71,72]. After pretesting the scale, certain items were adjusted based on participant feedback to better align with the respondents’ real-life situations. In this study, the internal consistency reliability (Cronbach’s α) of the scale was 0.899, with subscale reliabilities ranging from 0.83 to 0.87. The scale aligns with the cultural context and measurement needs of this study. The validity and reliability of the scale meet the standards required for psychological research and are suitable for measuring negative academic emotions in this study.

3.2.2. Self-Efficacy Scale

The study adopted the Self-Efficacy Scale developed by Schwarzer et al. [73], which consists of 7 items divided into two dimensions: academic ability self-efficacy and academic behavior self-efficacy. A 5-point Likert scale was used for responses. This scale was selected for its validated effectiveness in assessing students’ confidence levels and coping abilities [27,74]. Since this scale has been widely applied, only minor adjustments were made to several terms to better fit the cultural context and language habits of Chinese university students. In this study, the internal consistency reliability (Cronbach’s α) of the scale was 0.896, with subscale reliabilities reaching 0.91. The scale’s validity and reliability met the standards for psychological research and were suitable for measuring self-efficacy in this study.

3.2.3. Academic Procrastination Scale

The study used the Academic Procrastination Scale developed by McCloskey and Scielzo [75], which consists of 6 items divided into two dimensions: delayed planning and delayed execution. Responses were measured using a 5-point Likert scale. Originally developed by international scholars, this scale was adapted for the Chinese university context by revising some items to enhance clarity. For example, the term “postponing tasks” was specifically described as “leaving important tasks until the last minute.” In this study, the internal consistency reliability (Cronbach’s α) of the scale was 0.907, with subscale reliabilities ranging from 0.83 to 0.87. The validity and reliability of the scale meet the standards for psychological research and are suitable for measuring academic procrastination.

3.2.4. Goal Orientation Scale

The study employed the Student Goal Orientation Scale adapted by Jing Wu et al. [31], which consists of 4 items divided into two dimensions: cognitive recognition of goals and attitudes towards goals. Responses were measured using a 5-point Likert scale. This scale was chosen due to its compatibility with the study population, accurately reflecting students’ subjective endorsement and enthusiasm towards goal setting. Since the original scale was developed by Chinese scholars for research on Chinese university students, only minor adjustments were made to the wording of a few items [76,77,78]. In this study, the internal consistency reliability (Cronbach’s α) of the scale was 0.904, with subscale reliabilities ranging from 0.88 to 0.94. The validity and reliability of the scale meet the standards for psychological research and are appropriate for measuring goal orientation in academic settings.

4. Results Analysis

4.1. Measurement Model

In the evaluation of the measurement model, both reliability and validity tests are essential steps [79]. This study utilized SmartPLS 4.1.0.0 for analysis, applying partial least squares (PLS) to obtain the relevant reliability indicators. The significant advantage of PLS-SEM (partial least squares structural equation modeling) compared to other analytical methods is that it is particularly suitable for small sample sizes with non-normal distributions while also being prediction oriented. Moreover, PLS-SEM excels in handling complex latent variable path models, making it especially appropriate for the multivariable models employed in this study [80]. The advanced capabilities of PLS-SEM further enhance the robustness and validity of the results in this study [81].
Reliability was assessed through internal consistency coefficients, composite reliability (CR), and factor loadings. The standards for reliability are as follows: Cronbach’s α > 0.7, indicating high internal consistency; CR > 0.7, indicating high reliability of the measurement items [79]; and factor loadings > 0.7, indicating a strong relationship between the variable and the corresponding factor [82]. The reliability indices obtained in this study are shown in Table 1, confirming that the questionnaire meets the required reliability standards and is suitable for further structural equation model testing and analysis.
The validity of the model was assessed based on convergent validity and discriminant validity. For convergent validity, this study used the average variance extracted (AVE) to evaluate the degree of shared variance between the latent variables and their respective indicators. An AVE value greater than 0.5 indicates good convergent validity [79]. As shown in Table 1, all variables in this study had AVE values exceeding 0.5, confirming that the convergent validity passed the evaluation.
For discriminant validity, the Cross-Loadings method, the Fornell–Larcker criterion, and the Heterotrait–Monotrait ratio (HTMT) were employed. The Cross-Loadings method requires that the loading of an indicator on its corresponding latent variable should be higher than its loadings on other latent variables. According to the Fornell–Larcker criterion, the square root of the AVE should be greater than the correlation coefficient between the latent variables. The HTMT ratio is a more stringent measure for detecting poor discriminant validity, and a HTMT value below 0.90 is recommended [83]. The discriminant validity indices obtained in this study are shown in Table 2 and Table 3, indicating that the model demonstrates satisfactory discriminant validity between the constructs. Therefore, the scales in this study exhibit good validity.

4.2. Common Method Bias

Before evaluating the structural model, it is essential to check for common method bias. This study assessed common method bias using the Variance Inflation Factor (VIF) values of the inner model. Generally, all items should have VIF values of less than 3 [79]. As shown in Table 4, all VIF values were below 3, indicating that the model does not suffer from serious common method bias.

4.3. Structural Model

The R2 of endogenous latent variables represents the amount of variance explained by the model. According to Chin [79], R2 values of 0.19, 0.33, and 0.67 indicate low, moderate, and high explanatory power, respectively. As shown in Figure 2, the model demonstrates at least moderate explanatory power. Additionally, the Q2 value is used to assess the model’s predictive relevance, with Q2 > 0 indicating predictive relevance [84]. The Q2 values for academic procrastination (AP) and self-efficacy (SE) were 0.466 and 0.202, respectively, confirming that the model has predictive relevance. The f2 value reflects the effect size of each latent variable on the dependent variable [85,86,87]. As shown in Table 5, the effect sizes of Negative Academic Emotions (NAEs) on academic procrastination (AP) and self-efficacy (SE) are 0.372 and 0.402, respectively, both of which are considered large effects. The effect sizes of self-efficacy (SE) and sense of purpose (SOP) on academic procrastination (AP) are 0.070 and 0.061, respectively, indicating moderate effects. The interaction effect between sense of purpose and negative academic emotions (SOP × NAEs) on academic procrastination (AP) is 0.028, also indicating a moderate effect. These findings suggest that NAEs have a strong direct impact on both AP and SE, while the direct contributions of SE and SOP to AP are relatively smaller, further supporting the study’s hypotheses.
The size of the mediation effect can be interpreted using specific thresholds. Gaskin et al. (2023) proposed the following thresholds for interpreting mediation effect sizes: 0.01 (small effect), 0.04 (medium effect), and 0.09 (large effect). As shown in Table 5, the ν value for the mediation effect in this study reached 0.126, with a corresponding ν2 value of 0.016. Based on the criteria provided by Gaskin et al. [88], this indicates a small effect size.
The varying levels of SOP influence the strength of NAEs’ effect on AP. The interaction effect plot (Figure 3) visually illustrates the influence of NAEs on AP under high, medium, and low levels of the moderator SOP [89,90]. As shown in the figure, AP increases as NAEs increase, indicating a positive relationship between negative emotions and academic procrastination; in other words, the more negative the emotions, the more severe the procrastination behavior. When SOP is at a low level (red line), NAEs have the strongest impact on AP, with AP values increasing significantly as NAEs rise. When SOP is at a high level (green line), the impact of NAEs on AP is weaker, suggesting that students with a high sense of purpose exhibit a smaller increase in academic procrastination even in the presence of high negative emotions. This confirms that a high SOP buffers the relationship between negative emotions and academic procrastination, validating the presence of a moderating effect.

4.4. Hypothesis Testing Results

The significance of path coefficients was measured by conducting 5000 bootstrap resampling iterations, and hypothesis testing was determined by the significance of p-values. Hypotheses are supported when p < 0.05. As shown in Table 5, all hypothesis paths in this study were supported.

5. Discussion

The positive correlation between negative academic emotions and academic procrastination found in this study aligns with previous research. For instance, Eckert’s empirical study demonstrated that emotion regulation training can reduce procrastination by alleviating negative emotions [91]. These studies suggest that the more negative emotions individuals experience, the more likely they are to procrastinate, confirming the significant role of emotions in influencing procrastination behavior. However, most previous studies have focused on the correlational relationship between these factors [20,38]. The innovation of this study lies in exploring the mediating and moderating roles of self-efficacy and goal orientation in the relationship between negative emotions and procrastination using structural equation modeling. The findings of this study align with Bandura’s self-efficacy theory, Gross’s emotion regulation theory, and Eccles and Wigfield’s expectancy–value motivation theory [67,92]. The results show a significant positive correlation between negative emotions and academic procrastination, as well as a significant negative correlation between negative emotions and self-efficacy, but no significant correlation between negative emotions and goal orientation. Therefore, hypotheses H1 and H3 are supported. Self-efficacy and goal orientation were both negatively correlated with academic procrastination, supporting hypotheses H2 and H5. When self-efficacy was introduced as a mediating variable in the relationship between negative academic emotions, self-efficacy, and academic procrastination, the mediating effect of self-efficacy reached the upper threshold of significance, confirming the full mediating role of self-efficacy, thus supporting hypothesis H4. This study reveals that the correlation between negative academic emotions and procrastination is not entirely direct but can be mediated by self-efficacy, which indirectly influences procrastination. Goal orientation, on the other hand, works alongside negative emotions to affect academic procrastination.
Negative academic emotions represent adverse learning experiences, which negatively affect both self-efficacy and procrastination. According to Bandura’s self-efficacy theory, learners’ self-efficacy is closely related to their ability to cope with challenges [15,50,56]. Rakes emphasized in his research that negative learning experiences can undermine students’ subjective judgments about their abilities, reducing self-efficacy and thus increasing procrastination [93]. During the learning process, students with lower self-efficacy are more likely to focus on negative feedback, amplify the severity of negative situations, and experience diminished resilience, which increases the likelihood of procrastination triggered by negative emotions [39]. Conversely, students with high self-efficacy are more likely to focus on solving academic problems rather than being troubled by negative emotions. Trusting in their abilities, they can mobilize their cognitive resources to actively address academic challenges rather than passively avoiding or procrastinating [1]. Additionally, active problem-solving reinforces self-efficacy, creating a positive cycle of improved academic performance.
Consistent with Gustavson’s findings, this study also identified goal orientation as an important factor in preventing academic procrastination [10]. Goal orientation reflects students’ perception of the value and significance of a task, which is largely influenced by their subjective recognition of the task. Unlike self-efficacy, goal orientation is not significantly affected by negative emotions. Instead, it moderates the relationship between negative emotions and procrastination rather than mediating it. Goal orientation represents students’ intrinsic motivation to complete academic tasks and serves as a key source of behavioral motivation. The stronger the goal orientation, the higher the alignment between the value of completing the task and the student’s needs [94]. Negative emotions do not alter the value of the goal, meaning they do not diminish students’ drive to pursue the goal. Thus, even if students’ motivations decrease due to lowered self-efficacy following negative emotions, students with strong goal orientation can compensate for the loss in self-efficacy with their belief in the goal, maintaining their motivation and avoiding procrastination [95]. Furthermore, negative emotions may trigger a sense of urgency, prompting students to mobilize more cognitive resources to complete academic tasks, thereby reducing the likelihood of procrastination.
Furthermore, within the Chinese sociocultural context, factors such as a high-pressure academic environment, high parental expectations, and limited attention to mental health issues may exacerbate students’ negative emotions and academic procrastination [96,97]. The exam-oriented educational system and score-centric culture impose significant pressure on students in their pursuit of academic success, often at the expense of intrinsic motivation. This highly competitive environment leads Chinese students to experience greater anxiety and feelings of helplessness regarding academic challenges. Additionally, many Chinese parents tend to strongly associate their children’s current academic performance with future success, perceiving academic advancement as the only path in life [98,99]. Under the influence of both educational and survival anxieties, Chinese parents often hold high academic expectations for their children and may adopt strict parenting practices, such as hardship-based education, punitive discipline, and shame-based education [100,101]. These approaches increase children’s tendency toward self-blame and negative self-evaluation when facing challenging situations, which can trigger negative emotional reactions, undermine their self-efficacy, and ultimately contribute to higher levels of procrastination.
In summary, the results indicate that self-efficacy and goal orientation play important roles in moderating the causal path between negative academic emotions and procrastination. Both self-efficacy and goal orientation help students resist the negative effects of emotions on procrastination. These findings not only validate the initial hypothesis model of this study (Figure 1) but also support the viewpoints of expectancy–value theory, self-efficacy theory, and goal-setting theory. In educational practice, it is essential to protect and nurture students’ self-efficacy and guide them to develop a strong sense of goal orientation. Since negative academic emotions are a major cause of procrastination, schools could introduce emotion regulation training, such as cognitive-behavioral therapy, meditation, and mindfulness exercises, to help students better manage and regulate negative emotions, thereby minimizing their adverse impact on academic behavior.
Educators can also design and implement programs to enhance students’ self-efficacy and goal orientation. For example, fostering successful experiences, encouraging positive self-talk, and providing constructive feedback can boost students’ confidence. Additionally, assisting students in using the SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) goal-setting method, focusing on their academic growth, and providing timely positive feedback are effective interventions for addressing the challenges posed by negative emotions and reducing procrastination.

6. Conclusions

6.1. Main Findings

The primary objective of this study was to explore the impact mechanism of negative academic emotions on academic procrastination among Chinese university students, with self-efficacy acting as a mediating variable and goal orientation acting as a moderating variable, using structural equation modeling (SEM). The findings offer theoretical and practical insights into both the understanding of academic procrastination and the teaching–learning process. The key findings are as follows:
  • Negative academic emotions, such as anxiety and depression, significantly increase the likelihood of students procrastinating on academic tasks.
  • Self-efficacy plays a partial mediating role between negative emotions and academic procrastination. Higher self-efficacy can mitigate the negative impact of negative emotions on procrastination, highlighting the importance of enhancing self-efficacy as a strategy for reducing procrastination.
  • Goal orientation moderates the relationship between self-efficacy and academic procrastination. Students with high goal orientation are better able to control procrastination, even when experiencing negative emotions.

6.2. Theoretical and Practical Implications

Academic procrastination is influenced by multiple factors, including emotions, self-efficacy, and goal orientation. These factors not only have direct effects on procrastination but may also exert indirect effects through other variables. Common analytical methods, such as multiple regression, factor analysis, and path analysis, often fail to capture the complexity of mediating and moderating relationships. To address this issue, this study employed partial least squares structural equation modeling (PLS-SEM) to investigate the effects of multiple factors (negative academic emotions, self-efficacy, and goal orientation) on academic procrastination. PLS-SEM allows for the simultaneous handling of multiple dependent variables and the estimation of complex causal relationships, leading to more accurate theoretical model validation [102]. It also extracts common factors from highly correlated independent variables to construct latent variables, addressing multicollinearity and measurement error [103]. The application of PLS-SEM ensured the rigor, validity, and scientific soundness of this study’s theoretical model and conclusions [104].
Although emotion regulation and self-efficacy theories are well established in academia, they are often tested independently. This study is innovative in integrating these theories, particularly in the context of academic procrastination. Rather than focusing on the direct correlation between individual factors and procrastination, this study considered a more complex reality, combining the expectancy–value theory, emotion regulation theory, and self-efficacy theory into a comprehensive theoretical model (Figure 1). This allowed for an exploratory examination of the internal structure of these variables, expanding the existing theoretical framework and providing empirical support for the causes of academic procrastination.
In terms of practical applications for teaching and learning, the results indicate that cultivating and enhancing students’ self-efficacy can effectively mitigate the negative impact of emotions on procrastination, thereby improving academic performance. Educators should implement specific strategies, such as fostering successful experiences and gradually achieving task goals, to enhance students’ self-efficacy. Furthermore, goal orientation is crucial for maintaining academic motivation. Educators should focus on helping students develop awareness of short-term and long-term goals, using scientific strategies such as the SMART goal-setting method, in order to reduce procrastination. Emotion management techniques are also critical in reducing academic stress and improving academic performance. Educators can introduce emotion regulation techniques, such as mindfulness training or cognitive-behavioral therapy, to help students cope with academic pressure and maintain a positive learning attitude. These multi-layered interventions can effectively enhance students’ academic motivation and performance, promoting their overall development. In conclusion, the study fills existing research gaps and contributes to the understanding of academic procrastination both theoretically and practically. It offers methodological guidance for interventions aimed at reducing procrastination and provides valuable practical insights into improving student engagement, motivation, and academic achievement.

6.3. Limitations and Future Research Directions

Despite providing important insights into academic procrastination, this study has several limitations and offers directions for future research. While the study explores the effects of negative emotions, self-efficacy, and goal orientation on procrastination, other potentially relevant variables—such as environmental support and personality traits—are not fully investigated. Future research could incorporate these variables to further explore their relationships with academic procrastination. Additionally, the cross-sectional design of this study only captures correlations at a specific point in time, limiting the ability to infer causal relationships over a longer period. Moreover, while this study proposes theoretical interventions, their effectiveness in actual educational settings has not been fully tested. Future research could build on these findings by designing and testing specific intervention programs to evaluate their effectiveness in reducing procrastination across different educational environments, ultimately helping to develop more effective intervention strategies.

Author Contributions

Conceptualization, B.C. and S.L.; methodology, S.L.; software, S.L.; validation, H.Z. and S.L.; formal analysis, H.Z.; investigation, H.Z.; resources, H.Z.; data curation, H.Z.; writing—original draft preparation, B.C.; writing—review and editing, B.C.; visualization, B.C. and S.L.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Macao Polytechnic University grant number [RP/FCHS-02/2022 and RP/FCHS-01/2023].

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Research Committee of Macao Polytechnic University (project code: RP/FCHS-02/2022/E01, date: 17 June 2022). Ethics approval and consent to participate: this study received ethical approval. The researchers translated the scales into Indian Chinese. Afterward, the researchers established contact with researchers in higher education in the Guangdong–Hong Kong–Macao Greater Bay Area of China. Participation in this study was voluntary. Participants were invited through the Wenjuanxing app. They were informed that their data would be kept confidential before they began answering the questionnaire.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data used in this study is available upon request. Please contact the corresponding author via email for access.

Acknowledgments

The authors gratefully acknowledge the support of Macao Polytechnic University(RP/FCHS-02/2022 and RP/FCHS-01/2023).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Goroshit, M.; Hen, M. Academic procrastination and academic performance: Do learning disabilities matter? Curr. Psychol. 2021, 40, 2490–2498. [Google Scholar] [CrossRef]
  2. Klassen, R.M.; Ang, R.P.; Chong, W.H.; Krawchuk, L.L.; Huan, V.S.; Wong, I.Y.; Yeo, L.S. Academic procrastination in two settings: Motivation correlates, behavioral patterns, and negative impact of procrastination in Canada and Singapore. Appl. Psychol. 2010, 59, 361–379. [Google Scholar] [CrossRef]
  3. Meier, A.; Reinecke, L.; Meltzer, C.E. “Facebocrastination”? Predictors of using Facebook for procrastination and its effects on students’ well-being. Comput. Hum. Behav. 2016, 64, 65–76. [Google Scholar] [CrossRef]
  4. Shi, X.; Wang, S.; Liu, S.; Zhang, T.; Chen, S.; Cai, Y. Are procrastinators psychologically healthy? Association between psychosocial problems and procrastination among college students in Shanghai, China: A syndemic approach. Psychol. Health Med. 2019, 24, 570–577. [Google Scholar] [CrossRef]
  5. Bu, X.; Wu, L.; Wang, H. Impact of college students’ academic procrastination on subjective well-being. Soc. Behav. Pers. Int. J. 2021, 49, e9858. [Google Scholar] [CrossRef]
  6. Lei, Y.; Duan, C. Relationships among Chinese college students’ defensive pessimism, cultural values, and psychological health. Couns. Psychol. Q. 2016, 29, 335–355. [Google Scholar] [CrossRef]
  7. Zhang, J.; Liu, Y.; Sun, L. Psychological strain and suicidal ideation: A comparison between Chinese and US college students. Psychiatry Res. 2017, 255, 256–262. [Google Scholar] [CrossRef]
  8. Steel, P.; Klingsieck, K.B. Academic Procrastination: Psychological Antecedents Revisited. Aust. Psychol. 2016, 51, 36–46. [Google Scholar] [CrossRef]
  9. Brown, P.H.; Park, A. Education and poverty in rural China. Econ. Educ. Rev. 2002, 21, 523–541. [Google Scholar] [CrossRef]
  10. Gustavson, D.E.; Miyake, A. Academic procrastination and goal accomplishment: A combined experimental and individual differences investigation. Learn. Individ. Differ. 2017, 54, 160–172. [Google Scholar] [CrossRef]
  11. Haycock, L.A.; McCarthy, P.; Skay, C.L. Procrastination in college students: The role of self-efficacy and anxiety. J. Couns. Dev. 1998, 76, 317–324. [Google Scholar] [CrossRef]
  12. Kamber, E.; Fuke, T.S.S.; Alunni, M.; Mahy, C.E.V. Procrastination in early childhood: Associations with self-regulation, negative affectivity, and the home environment. Early Child. Res. Q. 2024, 66, 75–85. [Google Scholar] [CrossRef]
  13. Pollack, S.; Herres, J. Prior Day Negative Affect Influences Current Day Procrastination: A Lagged Daily Diary Analysis. Anxiety Stress Coping 2020, 33, 165–175. [Google Scholar] [CrossRef] [PubMed]
  14. Ferrari, J.R.; Parker, J.T.; Ware, C.B. Academic procrastination: Personality correlates with Myers-Briggs types, self-efficacy, and academic locus of control. J. Soc. Behav. Personal. 1992, 7, 495–502. [Google Scholar]
  15. Zimmerman, B.J.; Bandura, A.; Martinez-Pons, M. Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. Am. Educ. Res. J. 1992, 29, 663–676. [Google Scholar] [CrossRef]
  16. Klassen, R.M.; Krawchuk, L.L.; Rajani, S. Academic procrastination of undergraduates: Low self-efficacy to self-regulate predicts higher levels of procrastination. Contemp. Educ. Psychol. 2008, 33, 915–931. [Google Scholar] [CrossRef]
  17. Zarei, L.; Khoshouei, M.S. Relationship of academic procrastination with metacognitive beliefs, emotion regulation and tolerance of ambiguity in university students. Q. J. Res. Plan. High. Educ. 2023, 22, 113–130. [Google Scholar]
  18. Ljubin-Golub, T.; Petričević, E.; Rovan, D. The role of personality in motivational regulation and academic procrastination. Educ. Psychol. 2019, 39, 550–568. [Google Scholar] [CrossRef]
  19. Svartdal, F.; Dahl, T.I.; Gamst-Klaussen, T.; Koppenborg, M.; Klingsieck, K.B. How Study Environments Foster Academic Procrastination: Overview and Recommendations. Front. Psychol. 2020, 11, 540910. [Google Scholar] [CrossRef]
  20. Andini, A.Y.; Kurniawan, L. Academic Procrastination of Students in Online Learning. In Proceedings of the 1st Annual International Conference: A Transformative Education: Foundation & Innovation in Guidance and Counseling (AICGC 2022); Atlantis Press: Amsterdam, The Netherlands, 2023; pp. 3–10. [Google Scholar]
  21. Fentaw, Y.; Moges, B.T.; Ismail, S.M. Academic procrastination behavior among public university students. Educ. Res. Int. 2022, 2022, 1277866. [Google Scholar] [CrossRef]
  22. Chen, W.-W.; Yang, X.; Jiao, Z. Authoritarian parenting, perfectionism, and academic procrastination. Educ. Psychol. 2022, 42, 1145–1159. [Google Scholar] [CrossRef]
  23. Won, S.; Yu, S.L. Relations of perceived parental autonomy support and control with adolescents’ academic time management and procrastination. Learn. Individ. Differ. 2018, 61, 205–215. [Google Scholar] [CrossRef]
  24. Xie, M.; Feng, Y.; Zhang, Y.; Zhang, H.; Lin, D. Associations between trait and state perceived stress and daily moods: COVID-19 stressful experiences as a moderator. Curr. Psychol. 2024, 43, 14894–14908. [Google Scholar] [CrossRef]
  25. Luo, Q.; Chen, L.; Yu, D.; Zhang, K. The mediating role of learning engagement between self-efficacy and academic achievement among Chinese college students. Psychol. Res. Behav. Manag. 2023, 16, 1533–1543. [Google Scholar] [CrossRef]
  26. Liu, X.; Zhang, X.; Dang, Y.; Gao, W. Career education skills and career adaptability among college students in China: The mediating role of career decision-making self-efficacy. Behav. Sci. 2023, 13, 780. [Google Scholar] [CrossRef]
  27. Zhou, S.; Thompson, G.; Zhou, S. Transitioning from secondary school to an English-medium transnational university in China: A longitudinal study of student self-efficacy and motivational beliefs. Int. J. Biling. Educ. Biling. 2024, 27, 487–500. [Google Scholar] [CrossRef]
  28. Tamir, M.; Vishkin, A.; Gutentag, T. Emotion regulation is motivated. Emotion 2020, 20, 115. [Google Scholar] [CrossRef]
  29. Mohammadi Bytamar, J.; Saed, O.; Khakpoor, S. Emotion regulation difficulties and academic procrastination. Front. Psychol. 2020, 11, 524588. [Google Scholar] [CrossRef]
  30. Scheier, M.F.; Carver, C.S. Optimism, Pessimism, and Stress. In Encyclopedia of Stress, 2nd ed.; Fink, G., Ed.; Academic Press: New York, NY, USA, 2007; pp. 26–29. [Google Scholar] [CrossRef]
  31. Wu, J. The Influence of College Students’ Sense of Purpose on Learning Burnout: The Mediating Role of Academic Self-Efficacy. Master’s Thesis, Fujian Normal University, Fuzhou, China, 2022. [Google Scholar]
  32. Bandura, A.; Adams, N.E.; Hardy, A.B.; Howells, G.N. Tests of the generality of self-efficacy theory. Cogn. Ther. Res. 1980, 4, 39–66. [Google Scholar] [CrossRef]
  33. Sirois, F.M. Procrastination and stress: A conceptual review of why context matters. Int. J. Environ. Res. Public Health 2023, 20, 5031. [Google Scholar] [CrossRef]
  34. Johansson, F.; Rozental, A.; Edlund, K.; Côté, P.; Sundberg, T.; Onell, C.; Rudman, A.; Skillgate, E. Associations between procrastination and subsequent health outcomes among university students in Sweden. JAMA Netw. Open 2023, 6, e2249346. [Google Scholar] [CrossRef] [PubMed]
  35. Gadosey, C.K.; Schnettler, T.; Scheunemann, A.; Bäulke, L.; Thies, D.O.; Dresel, M.; Fries, S.; Leutner, D.; Wirth, J.; Grunschel, C. Vicious and virtuous relationships between procrastination and emotions: An investigation of the reciprocal relationship between academic procrastination and learning-related anxiety and hope. Eur. J. Psychol. Educ. 2024, 39, 2005–2031. [Google Scholar] [CrossRef]
  36. Bosse, T.; Pontier, M.; Treur, J. A computational model based on Gross’ emotion regulation theory. Cogn. Syst. Res. 2010, 11, 211–230. [Google Scholar] [CrossRef]
  37. Lee, M.; Pekrun, R.; Taxer, J.L.; Schutz, P.A.; Vogl, E.; Xie, X. Teachers’ emotions and emotion management: Integrating emotion regulation theory with emotional labor research. Soc. Psychol. Educ. 2016, 19, 843–863. [Google Scholar] [CrossRef]
  38. Rothblum, E.D.; Solomon, L.J.; Murakami, J. Affective, cognitive, and behavioral differences between high and low procrastinators. J. Couns. Psychol. 1986, 33, 387–394. [Google Scholar] [CrossRef]
  39. Baumeister, R.F. Esteem threat, self-regulatory breakdown, and emotional distress as factors in self-defeating behavior. Rev. Gen. Psychol. 1997, 1, 145–174. [Google Scholar] [CrossRef]
  40. Tice, D.M.; Bratslavsky, E. Giving in to Feel Good: The Place of Emotion Regulation in the Context of General Self-Control. Psychol. Inq. 2000, 11, 149–159. [Google Scholar] [CrossRef]
  41. Fee, R.L.; Tangney, J.P. Procrastination: A means of avoiding shame or guilt? J. Soc. Behav. Personal. 2000, 15, 167. [Google Scholar]
  42. Ariati, J.; Nasution, S.A.; Laras, Q.; Fathiawati, A.S.; Panggabean, E.C. An Individual Positive Emotion Exercise: Its Influence on Self-Efficacy and Procrastination of Nursing Students. In Proceedings of the 3rd Asean Conference on Psychology, Counselling, and Humanities (ACPCH 2017), Malang, Indonesia, 21–22 October 2017; Volume 133, pp. 9–11. [Google Scholar]
  43. Steel, P. The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychol. Bull. 2007, 133, 65–94. [Google Scholar] [CrossRef]
  44. Beswick, G.; Rothblum, E.D.; Mann, L. Psychological antecedents of student procrastination. Aust. Psychol. 1988, 23, 207–217. [Google Scholar] [CrossRef]
  45. Bandura, A. Self-efficacy: Toward a unifying theory of behavioral change. Psychol. Rev. 1977, 84, 191. [Google Scholar] [CrossRef] [PubMed]
  46. Wang, M.; Liu, K. Self-recognition, self-efficacy, and confidence intervention of kunjing children without sufficient parental care. Res. Soc. Work. Pract. 2023, 33, 849–860. [Google Scholar] [CrossRef]
  47. Doucette, E.J.; Fullerton, M.M.; Pateman, M.; Lip, A.; Houle, S.K.; Kellner, J.D.; Leal, J.; MacDonald, S.E.; McNeil, D.; Tyerman, J. Development and evaluation of virtual simulation games to increase the confidence and self-efficacy of healthcare learners in vaccine communication, advocacy, and promotion. BMC Med. Educ. 2024, 24, 190. [Google Scholar] [CrossRef]
  48. Li, F.; Wang, G.; Zhu, D.; Liu, S.; Liao, J.; Zhang, S.; Li, J. Parental neglect and short-form video application addiction in Chinese adolescents: The mediating role of alexithymia and the moderating role of refusal self-efficacy. Child Abus. Negl. 2023, 143, 106345. [Google Scholar] [CrossRef] [PubMed]
  49. Chen, S. Structural modeling of Chinese students’ academic achievement identity and basic psychological needs: Do academic self-efficacy, and mindfulness play a mediating role? BMC Psychol. 2024, 12, 142. [Google Scholar] [CrossRef] [PubMed]
  50. Jia, F.; Meng, J.; Ma, Y.; Mao, Y. Flow experience and self-efficacy in undergraduates’ English learning: A weekly diary investigation with cross-lagged panel modeling approach. System 2024, 123, 103312. [Google Scholar] [CrossRef]
  51. Sun, Y.; Lu, X.; Cui, J.; Du, K.; Xie, S. Effects of vicarious experiences of nature, environmental beliefs, and attitudes on adolescents’ environmental behavior. Environ. Educ. Res. 2024, 30, 926–940. [Google Scholar] [CrossRef]
  52. Kong, A.; Teng, M.F. The operating mechanisms of self-efficacy and peer feedback: An exploration of L2 young writers. Appl. Linguistics Rev. 2023, 14, 297–328. [Google Scholar] [CrossRef]
  53. Lu, J.; Wang, B.; Dou, X.; Yu, Y.; Zhang, Y.; Ji, H.; Chen, X.; Sun, M.; Duan, Y.; Pan, Y. Moderating effects of perceived social support on self-efficacy and psychological well-being of Chinese nurses: A cross-sectional study. Front. Public Health 2023, 11, 1207723. [Google Scholar] [CrossRef]
  54. Guo, X.; Peng, Q.; Wu, S.; Li, Y.; Dong, W.; Tang, H.; Lu, G.; Chen, C. Perceived parenting style and Chinese nursing undergraduates’ learning motivation: The chain mediating roles of self-efficacy and positive coping style. Nurse Educ. Pract. 2023, 68, 103607. [Google Scholar] [CrossRef]
  55. He, X.; Fang, S.; Du, L. Study on the Relationship Between Self-Efficacy and Psychological Well-Being Among Chinese College Students. Stud. Psychol. Sci. 2023, 1, 53–58. [Google Scholar] [CrossRef]
  56. Baluszek, J.B.; Brønnick, K.K.; Wiig, S. The relations between resilience and self-efficacy among healthcare practitioners in context of the COVID-19 pandemic—A rapid review. Int. J. Health Gov. 2023, 28, 152–164. [Google Scholar] [CrossRef]
  57. Dzewaltowski, D.A.; Noble, J.M.; Shaw, J.M. Physical activity participation: Social cognitive theory versus the theories of reasoned action and planned behavior. J. Sport Exerc. Psychol. 1990, 12, 388. [Google Scholar] [CrossRef]
  58. Luszczynska, A.; Schwarzer, R. Social Cognitive Theory; Faculty of Health Sciences: London, UK, 2015; pp. 225–251. [Google Scholar]
  59. Maddux, J.E. Self-Efficacy Theory: An Introduction. In Self-Efficacy, Adaptation, and Adjustment: Theory, Research, and Application; Springer: Boston, MA, USA, 1995; pp. 3–33. [Google Scholar]
  60. Cerino, E.S. Relationships between academic motivation, self-efficacy, and academic procrastination. Psi Chi J. Psychol. Res. 2014, 19, 156–163. [Google Scholar] [CrossRef]
  61. Jeong, Y.H.; Healy, L.C.; McEwan, D. The application of goal setting theory to goal setting interventions in sport: A systematic review. Int. Rev. Sport Exerc. Psychol. 2023, 16, 474–499. [Google Scholar] [CrossRef]
  62. Locke, E.A.; Latham, G.P. New directions in goal-setting theory. Curr. Dir. Psychol. Sci. 2006, 15, 265–268. [Google Scholar] [CrossRef]
  63. Lunenburg, F.C. Goal-setting theory of motivation. Int. J. Manag. Bus. Adm. 2011, 15, 1–6. [Google Scholar]
  64. Muñoz-Olano, J.F.; Hurtado-Parrado, C. Effects of goal clarification on impulsivity and academic procrastination of college students. Rev. Latinoam. Psicol. 2017, 49, 173–181. [Google Scholar] [CrossRef]
  65. Theobald, M.; Bäulke, L.; Bellhäuser, H.; Breitwieser, J.; Mattes, B.; Brod, G.; Daumiller, M.; Dresel, M.; Liborius, P.; Nückles, M. A multi-study examination of intra-individual feedback loops between competence and value beliefs, procrastination, and goal achievement. Contemp. Educ. Psychol. 2023, 74, 102208. [Google Scholar] [CrossRef]
  66. Kaftan, O.J.; Freund, A.M. A Motivational Perspective on Academic Procrastination: Goal Focus Affects How Students Perceive Activities While Procrastinating. Motiv. Sci. 2019, 5, 135–156. [Google Scholar] [CrossRef]
  67. Frenzel, A.C.; Goetz, T.; Stockinger, K. Emotions and emotion regulation. In Handbook of Educational Psychology; Routledge: London, UK, 2024; pp. 219–244. [Google Scholar]
  68. Chen, W.; Zhang, G.; Tian, X.; Wang, L. Psychometric properties and measurement invariance of the emotion regulation questionnaire in Chinese left-behind children. Curr. Psychol. 2023, 42, 8833–8843. [Google Scholar] [CrossRef]
  69. Liu, Q.; Zhang, R.; Song, G.J. The impact of academic engagement and academic self-concept to academic emotions on junior high school students. J. Educ. New Century 2014, 2, 6–11. [Google Scholar]
  70. Shen, K. The dark triad and depressive symptoms among chinese adolescents: Moderated mediation models of age and emotion regulation strategies. Curr. Psychol. 2023, 42, 30949–30958. [Google Scholar] [CrossRef]
  71. Ren, Z.; Xin, Y.; Wang, Z.; Liu, D.; Ho, R.C.; Ho, C.S. What factors are most closely associated with mood disorders in adolescents during the COVID-19 pandemic? A cross-sectional study based on 1771 adolescents in Shandong Province, China. Front. Psychiatry 2021, 12, 728278. [Google Scholar] [CrossRef]
  72. Li, S.; Wu, H.; Wang, Y. Positive emotions, self-regulatory capacity, and EFL performance in the Chinese senior high school context. Acta Psychol. 2024, 243, 104143. [Google Scholar] [CrossRef] [PubMed]
  73. Schwarzer, R.; Jerusalem, M. Generalized Self-Efficacy Scale. 1995. Available online: https://www.researchgate.net/publication/304930542_Generalized_Self-Efficacy_Scale (accessed on 1 October 2024).
  74. Kang, L.; Li, C.; Chen, D.; Bao, X. Parental involvement, academic self-efficacy, and depression on academic performance among Chinese students during COVID-19 pandemic. Psychol. Res. Behav. Manag. 2024, 17, 201–216. [Google Scholar] [CrossRef]
  75. McCloskey, J.; Scielzo, S.A. Finally!: The development and validation of the academic procrastination scale. 2015; under review. [Google Scholar]
  76. Liu, Q.; Du, X.; Lu, H. Teacher support and learning engagement of EFL learners: The mediating role of self-efficacy and achievement goal orientation. Curr. Psychol. 2023, 42, 2619–2635. [Google Scholar] [CrossRef]
  77. Hemi, A.; Madjar, N.; Rich, Y. Perceived peer and teacher goals: Relationships with students’ academic achievement goals. J. Exp. Educ. 2023, 91, 145–165. [Google Scholar] [CrossRef]
  78. Liu, X.; Zhang, Y.; Cao, X.; Gao, W. Does anxiety consistently affect the achievement goals of college students? A four-wave longitudinal investigation from China. Curr. Psychol. 2024, 43, 10495–10508. [Google Scholar] [CrossRef]
  79. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
  80. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 2011, 19, 139–152. [Google Scholar] [CrossRef]
  81. Memon, M.A.; Ramayah, T.; Cheah, J.-H.; Ting, H.; Chuah, F.; Cham, T.H. PLS-SEM statistical programs: A review. J. Appl. Struct. Equ. Model. 2021, 5, 1–14. [Google Scholar] [CrossRef] [PubMed]
  82. Bagozzi, R.P. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error: A Comment; SAGE Publications: Los Angeles, CA, USA, 1981. [Google Scholar]
  83. Henseler, J.; Ringle, C.M.; Sarstedt, M.J. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef]
  84. Hair, J.F.; Ringle, C.M.; Sarstedt, M. Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Plan. 2013, 46, 1–12. [Google Scholar] [CrossRef]
  85. Cheah, J.-H.; Amaro, S.; Roldán, J.L. Multigroup analysis of more than two groups in PLS-SEM: A review, illustration, and recommendations. J. Bus. Res. 2023, 156, 113539. [Google Scholar] [CrossRef]
  86. Sarstedt, M.; Hair, J.F., Jr.; Ringle, C.M. “PLS-SEM: Indeed a silver bullet”—Retrospective observations and recent advances. J. Mark. Theory Pract. 2023, 31, 261–275. [Google Scholar] [CrossRef]
  87. Strzelecki, A. To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interact. Learn. Environ. 2023, 2, 1–14. [Google Scholar] [CrossRef]
  88. Gaskin, J.; Ogbeibu, S.; Lowry, P.B. Demystifying prediction in mediation research and the use of specific indirect effects and indirect effect sizes. In Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications; Springer: Cham, Switzerland, 2023; pp. 209–228. [Google Scholar]
  89. Iranmanesh, M.; Ghobakhloo, M.; Foroughi, B.; Nilashi, M.; Yadegaridehkordi, E. Factors influencing attitude and intention to use autonomous vehicles in Vietnam: Findings from PLS-SEM and ANFIS. Inf. Technol. People 2024, 37, 2223–2246. [Google Scholar] [CrossRef]
  90. Zhu, B.; Xu, Y.; Ge, H.; Wang, S.; Wang, W.; Li, B.; Xu, H. Theoretical study of lactic acid-based deep eutectic solvents dissociation of hemicellulose with different hydrogen bonding acceptors. Int. J. Biol. Macromol. 2023, 244, 125342. [Google Scholar] [CrossRef]
  91. Zhao, C.; Li, J.; Seung-Yong, K. The Structural Relationships among Academic Pressure, Independent Learning Ability, and Academic Self-Efficacy. Iran. J. Public Health 2023, 52, 1008–1018. [Google Scholar] [CrossRef]
  92. Aldrup, K.; Carstensen, B.; Klusmann, U. The role of teachers’ emotion regulation in teaching effectiveness: A systematic review integrating four lines of research. Educ. Psychol. 2024, 59, 89–110. [Google Scholar] [CrossRef]
  93. Rabin, L.A.; Fogel, J.; Nutter-Upham, K.E. Academic procrastination in college students: The role of self-reported executive function. J. Clin. Exp. Neuropsychol. 2011, 33, 344–357. [Google Scholar] [CrossRef]
  94. Grunschel, C.; Patrzek, J.; Fries, S. Exploring reasons and consequences of academic procrastination: An interview study. Eur. J. Psychol. Educ. 2013, 28, 841–861. [Google Scholar] [CrossRef]
  95. Seo, E.H. The relationship of procrastination with a mastery goal versus an avoidance goal. Soc. Behav. Personal. Int. J. 2009, 37, 911–919. [Google Scholar] [CrossRef]
  96. Sze, K.Y.; Lee, E.K.; Chan, R.H.; Kim, J.H. Prevalence of negative emotional eating and its associated psychosocial factors among urban Chinese undergraduates in Hong Kong: A cross-sectional study. BMC Public Health 2021, 21, 583. [Google Scholar] [CrossRef] [PubMed]
  97. Cui, G.; Yin, Y.; Li, S.; Chen, L.; Liu, X.; Tang, K.; Li, Y. Longitudinal relationships among problematic mobile phone use, bedtime procrastination, sleep quality and depressive symptoms in Chinese college students: A cross-lagged panel analysis. BMC Psychiatry 2021, 21, 449. [Google Scholar] [CrossRef] [PubMed]
  98. Zheng, S.; Liu, H.; Yao, M. Social Support From Parents and Teachers and Adolescents’ Subjective Well-Being: Mediating Effect of Cognitive Regulatory Learning and Academic Procrastination. Child Indic. Res. 2023, 16, 485–508. [Google Scholar] [CrossRef]
  99. Zhu, Y.; Wang, Q.; Liu, J.; Huang, J.J.A.o.P.N. Parental psychological control and depression, anxiety among adolescents: The mediating role of bedtime procrastination and moderating role of neuroticism. Arch. Psychiatr. Nurs. 2024, 51, 1–9. [Google Scholar] [CrossRef]
  100. Yang, B.; Chen, B.-B.; Qu, Y.; Zhu, Y.J. Impacts of parental burnout on Chinese youth’s mental health: The role of parents’ autonomy support and emotion regulation. J. Youth Adolesc. 2021, 50, 1679–1692. [Google Scholar] [CrossRef]
  101. Hou, Y.; Xiao, R.; Yang, X.; Chen, Y.; Peng, F.; Zhou, S.; Zeng, X.; Zhang, X. Parenting style and emotional distress among Chinese college students: A potential mediating role of the Zhongyong thinking style. Front. Psychol. 2020, 11, 1774. [Google Scholar] [CrossRef]
  102. Hooper, D.; Coughlan, J.; Mullen, M. Structural equation modelling: Guidelines for determining model fit. Electron. J. Bus. Res. Methods 2008, 6, 53–60. [Google Scholar]
  103. Kline, R.B. Principles and Practice of Structural Equation Modeling; Guilford Publications: New York, NY, USA, 2023. [Google Scholar]
  104. Danner, D.; Hagemann, D.; Fiedler, K. Mediation analysis with structural equation models: Combining theory, design, and statistics. Eur. J. Soc. Psychol. 2015, 45, 460–481. [Google Scholar] [CrossRef]
Education 14 01232 g001
Figure 1. A hypothetical model of influencing factors of academic procrastination behavior.
Figure 1. A hypothetical model of influencing factors of academic procrastination behavior.
Education 14 01232 g001
Education 14 01232 g002
Figure 2. Structural model results.
Figure 2. Structural model results.
Education 14 01232 g002
Education 14 01232 g003
Figure 3. Interaction effect plot.
Figure 3. Interaction effect plot.
Education 14 01232 g003
Table 1. Reliability and validity analysis.
Table 1. Reliability and validity analysis.
ConstructsTitle ItemsCronbach’s AlphaCRAVE
AP50.9090.9330.735
NAE50.7730.8470.530
SE30.8010.8830.716
SOP50.8590.9160.784
Table 2. Fornell–Larcker Criterion.
Table 2. Fornell–Larcker Criterion.
APNAESESOP
AP0.857
NAE0.7110.728
SE−0.656−0.5350.846
SOP−0.616−0.4960.6960.886
Table 3. Heterotrait–Monotrait Ratio.
Table 3. Heterotrait–Monotrait Ratio.
APNAESESOPSOP × NAE
AP
NAE0.846
SE0.7680.669
SOP0.6950.5910.838
SOP × NAE0.3610.3340.3470.174
Table 4. Variance Inflation Factor.
Table 4. Variance Inflation Factor.
VIF
NAEs → AP1.510
NAEs → SE1.000
SE → AP2.233
SOP → AP2.043
SOP × NAEs → AP1.142
Table 5. Model Hypothesis Testing Results.
Table 5. Model Hypothesis Testing Results.
HypothesisOMSTDEVPf2Result
H10.4490.4500.0560.0000.372Accept
H2−0.535−0.5370.0490.0000.07Accept
H3−0.236−0.2340.0670.0000.402Accept
H40.1260.1260.0390.001N/A Accept
H5−0.113−0.1150.0480.0200.028Accept
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chen, B.; Zhang, H.; Li, S. The Impact Mechanism of Negative Academic Emotions on Academic Procrastination: The Mediating and Moderating Roles of Self-Efficacy and Goal Orientation. Educ. Sci. 2024, 14, 1232. https://doi.org/10.3390/educsci14111232

AMA Style

Chen B, Zhang H, Li S. The Impact Mechanism of Negative Academic Emotions on Academic Procrastination: The Mediating and Moderating Roles of Self-Efficacy and Goal Orientation. Education Sciences. 2024; 14(11):1232. https://doi.org/10.3390/educsci14111232

Chicago/Turabian Style

Chen, Bowen, Hongfeng Zhang, and Sigan Li. 2024. "The Impact Mechanism of Negative Academic Emotions on Academic Procrastination: The Mediating and Moderating Roles of Self-Efficacy and Goal Orientation" Education Sciences 14, no. 11: 1232. https://doi.org/10.3390/educsci14111232

APA Style

Chen, B., Zhang, H., & Li, S. (2024). The Impact Mechanism of Negative Academic Emotions on Academic Procrastination: The Mediating and Moderating Roles of Self-Efficacy and Goal Orientation. Education Sciences, 14(11), 1232. https://doi.org/10.3390/educsci14111232

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop