Sage Journals: Discover world-class research

Abstract

Student satisfaction is a crucial indicator reflecting the sustainable development of both students and universities. While a substantial body of research exists on student satisfaction, the degree to which university students are content with their academic development has not been fully explored. Aiming to promote students’ academic development and sustainability of learning journey, this study examined the interplay among academic development satisfaction, interpersonal relationships, and social support. Utilizing a stratified cluster sampling technique, 846 valid answers were collected from a leading public research university in China. Descriptive analysis, Pearson’s correlation analysis, structural equation modeling analysis and bias-corrected percentile bootstrap method were conducted using SPSS 26.0. and AMOS 21.0. The findings indicate a significant positive correlation among academic development satisfaction, interpersonal relationships, and social support. Social support serves as a partial mediator between academic development satisfaction and interpersonal relationships, accounting for 12.96% of the total effect. These findings suggest that a nurturing interpersonal environment and robust social support, can enhance university students’ academic satisfaction, thereby fostering the sustainable development of both students and institutions.

Plain Language Summary

Improving University Students’ Academic Development Satisfaction by Creating A More Harmonious and Supportive Interpersonal Environment

Sustainable development is recognized by many international organizations as a strategic idea for global, human, and future progress, which ultimately serve a long-term purpose: sustainability. In higher education, the dropout is a sign of unsustainable development. Given that student satisfaction is closely with the dropout, it a crucial indicator reflecting the sustainable development of both students and universities. Achieving academic goals/expectations is the primary need of college and university students pursuing higher education. Thus, the research on student satisfaction should pay attention to whether and how students’ needs/expectations for their own academic development are met in higher education. While a substantial body of research exists on student satisfaction, there are few studies that directly address the students’ core need and individual learners’ educational goals. In order to address this research gap and improve students’ academic development satisfaction, this study was conducted. Based on relevant research and theory, two research hypotheses are established to examine the relationship among interpersonal relationships, social support and students’ academic development satisfaction. Utilizing a stratified cluster sampling technique, 846 valid questionnaires were collected from a leading public research university in China. The findings indicate a significant positive correlation among academic development satisfaction, interpersonal relationships and social support. Interpersonal relationships can not only indirectly affect academic development satisfaction of university students through perceived social support, but also directly affecting it. Social support serves as a mediator between them. These findings suggest that a nurturing interpersonal environment and robust social support, can enhance university students’ academic satisfaction, thereby fostering the sustainable development of both students and institutions.

Keywords

academic development satisfaction interpersonal relationship social support meditating effect student satisfaction

Introduction

Sustainable development is recognized by many international organizations as a strategic idea for global, human, and future progress (L. Zhao, Zhu, et al., 2023). In the Brundtland Commission’s 1987 report, sustainable development was defined as “development that fulfills the requirements of the present without compromising future generations’ capacity to satisfy their own needs” (Moltesen & Bjørn, 2018, p. 44). Today, its goals have been developed from protecting ecological environment to balancing the economic success, environmental quality, and social equality for all in the present and the future (Raza et al., 2023). Olawumi and Chan (2018) argue that these goals ultimately serve a long-term purpose: sustainability. Thus, sustainable development can be understood as the social process that involves options and decisions aiming for sustainability (Pedro et al., 2023). This sustainability is comprehensive and involves all aspects of human activity.

In higher education, the dropout is a sign of unsustainable development. Numerous studies (Duque, 2014; Eather et al., 2022; van Rooij et al., 2018) emphasize the strong correlations between student satisfaction and student retention. Low student satisfaction is likely to lead to dropout behavior. In addition, student satisfaction influences various institutional goals related to sustainable development, including student motivation, retention, recommendations to prospective students, recruitment initiatives, funding, performance management, and even institutional reputation (Douglas et al., 2006; Nastasić et al., 2019; Walker et al., 2016). Therefore, student satisfaction is a predictor of sustainable development for both students and universities.

Satisfaction is defined as the degree to which an individual’s actual experiences and outcomes measure up against their prior expectations (Oliver & DeSarbo, 1988). Tinto (1975) believes it is the interplay between the individual’s goal commitments and his institutional commitments that determines whether or not the student decides to persist in his college. In other words, if a student thinks that the performance of the institution is no longer meeting its commitment that help achieve his/her educational goal, he/she will not continue the commitments of completing college and investing their time and money in the institution. Thus, the dropout behavior caused by low satisfaction reflects students’ unmet developmental needs by education institutions.

Achieving academic goals/expectations is the primary need of college students pursuing higher education. As Pascarella and Terenzini (1980) noted, each student enters university with their own academic commitments and objectives in mind. Moreover, Arnett (2015) addresses that heightened self-awareness is a distinguishing feature of psychological development in individuals aged 18 to 25, a phase termed “emerging adulthood” by him. Therefore, the research on student satisfaction should pay attention to whether and how students’ needs/expectations for their own academic development are met in higher education.

While existing research on student satisfaction underscores the pivotal role of students and highlights their experiences throughout their academic journey, it predominantly concentrates on assessing “external elements” (e.g., curriculum, teaching, accommodation, facilities in the higher education systems) and the dynamics of interactions with “others” (internal university groups apart from the student body; Cant et al., 2023; Nastasić et al., 2019; Rossini et al., 2021; Wong & Chapman, 2023; F. F. Zhao, Wang, & Ping, 2023). There are few studies that directly address the students’ core need and individual learners’ educational goals. As a result, students’ expectations and desires concerning their own academic growth can be overshadowed. This can cause student satisfaction surveys to fail in accurately reflecting the most desired developmental needs of students, instead student feedbacks are used to serve the interests of the university itself. This approach subtly risks making students a secondary priority.

In order to address this research gap and invisible risk, this study adopted a new perspective of self to examine how the academic development need of university students were met. Therefore, in this study, academic development satisfaction is defined as students’ satisfaction with their own academic development, which involves the self-assessment of their academic performance. According to existing literature, this study captured two other variables—interpersonal relationships and social support—and examined the relationship between them and students’ academic development satisfaction.

Relevant Theory, Literature Review, and Research Hypotheses

Student Satisfaction and Interpersonal Relationships

The college impact theory indicates that student personal traits and higher education environment have a substantial influence on various aspects of students’ current and future development, including knowledge, skills, behaviors, attitudes, values, beliefs, interests, and even cognitive preferences (Astin, 1970a; Bao, 2014). As a classic model of college impact theory, Astin’s (1970a, 1970b) input-environment-output (I-E-O) framework argues that, compared to inherent personal traits (input), the college environment (environment) has a more significant effect on students’ growth and development during their time in higher education (output).

Interpersonal interaction is an important variable among college environment factors. Various college impact models, such as those conceived by Spady (1970), Tinto (1975), Pascarella (1985), and Cabrera et al. (1993), indicate that social interactions greatly influence academic development. Notably, compared to other college environment factors, faculty–student and peer interactions have a greater impact on academic and cognitive development, as well as socio-psychological and emotional attitudes (Pascarella et al., 2004), such as student satisfaction (Pascarella, 1980). Spady’s (1970) model also explicitly states that social integration directly affects student satisfaction. Weidman (1989) extended the range of groups that interact between students from teachers and peers inside to their parents and others outside the school. Kim and Sax (2014) suggest that students’ perceptions or evaluations of their academic development, categorized as socio-psychological outputs in the I-E-O model, are shaped by their interpersonal interactions with faculty. Hence, according to the college impact theory, interpersonal relationships can influence not only student satisfaction, but also their perception of their actual academic development.

In addition, a recent study by Wong and Chapman (2023) revealed that every facet of student satisfaction—from program and teaching quality to campus facilities and overall university experience—correlates significantly with interpersonal interactions. In light of recent advancements in educational technology and the implications of the COVID-19 pandemic, researchers have increasingly focused on the connection between interpersonal interaction and student satisfaction in online learning environments (Chang & Smith, 2008; Wong & Chapman, 2023). Nonetheless, Kuo et al. (2014) contend that high-quality interaction is essential across all educational formats, be they technology-driven or traditional. For instance, Johnson et al. (2014) found that irrespective of the instructional mode—whether face-to-face or online—students’ interpersonal interactions have a significant impact on their course satisfaction.

In summary, university students’ academic development satisfaction acts as an outcome variable in the socio-psychological dimension of higher education, and can be influenced by interpersonal relationships as an environment variable in higher education context. According to this, we are positioned to formulate the following research hypothesis:

H₁: Students’ interpersonal relationships have a positive effect on their academic development satisfaction in higher education field.

Social Support as a Mediator: Students’ Academic Development Satisfaction and Interpersonal Relationships

In addition to interpersonal relationships, social support has also been found to be related to student satisfaction (Sivandani et al., 2013) and academic self-perception such as, academic adjustment (Rodríguez et al., 2012), academic self-efficacy (Xu et al., 2023), and academic self-concept (Rodríguez et al., 2012). Gutiérrez et al. (2017) conducted a questionnaire survey of 2,028 students aged 14 to 22 in Angola, discovering a significant correlation between perceived academic support from peers, family, and teachers and students’ engagement and satisfaction with school. Lent et al. (2007) employed structural equation modeling (SEM) to discern that, as a socio-cognitive factor, the perception of support in the campus environment notably influences the academic satisfaction levels of university students. L. Fang et al. (2016) found that perceived social support significantly impacts the academic satisfaction of university students. Ramos-Díaz et al. (2016) reviewed previous studies and found that adolescents who perceived greater support from teachers, family, peers, and other sources within the school environment tended to have better self-perception and self-evaluation. Their study also revealed that perceived support from peers and family significantly influenced students’ general self-concept. A study by Yamada et al. (2014) also indicated a significant correlation between peer support and academic self-perception, suggesting that low peer support can lead students to perceive their own academic progress negatively.

Additionally, social support is closely linked to interpersonal relationships, which can be divided into two aspects: quantity and quality (House et al., 1988). Researchers categorize types of social support based on specific groups of individual interactions, for instance, teacher support, peer/classmate support, and family/parental support (Danielsen et al., 2009; Mishra, 2020; Wentzel, 1999; Zimet et al., 1988), highlighting how the quantity and structure of interpersonal ties are potential avenues for obtaining social support. Nowadays, social networking sites are popular, and the interpersonal relationships built through virtual networks will provide them with additional social support (Hsu et al., 2018). However, not all interpersonal connections can provide support, such as people with “weak ties” at the network’s edge (Granovetter, 1973). People obtain more meaningful and effective social or emotional support from close relationships, such as those among family and friends (Mishra, 2020). Consequently, the focus has shifted from the patterns and scope of interpersonal interactions to their quality (Berkman et al., 2000; Jaggars & Xu, 2016; Johnson et al., 2014; Liou & Canrinus, 2020; Wong & Chapman, 2023). For instance, Jaggars and Xu (2016) reported a significant positive correlation between the quality of interactions in courses and students’ academic performance. Baker-Doyle (2012) also noted a positive correlation between the extent of interpersonal connections and perceived social support. High-quality interpersonal relationships can be characterized by positive emotional experiences and appropriate frequencies of interaction (Lai & Gwung, 2013). Relationships that offer good experiences and regular contact are likely to provide individuals with more substantial and effective social support.

In short, university students’ academic development satisfaction is not only a part of student satisfaction but also serves as a representation of students’ academic self-perception or self-evaluation. Given the substantial impact of social support on students’ satisfaction and academic self-perception, as previously discussed, it is reasonable to speculate that social support influences university students’ academic development satisfaction. Social support, identified as one of the most commonly accessed and effective external resources (Xu et al., 2023), is either obtained or perceived via the interpersonal connections of university students. With this understanding, we propose the following research hypothesis:

H₂: Social support plays a mediating role in the relationship between students’ interpersonal relationships and their academic development satisfaction in higher education field.

Materials and Methods

Participants and Procedure

The study took place at a leading research university situated in eastern China from April 2022 to June 2022. The university is government-funded and ranks among the elite universities supported by the national “211 Project,” an initiative by the central government aimed at bolstering select universities with the aspiration of establishing world-class institutions. As of 2022, the university has 36 departments with a total of 27,876 full-time undergraduate students and 16,141 graduate students. We employed a stratified cluster sampling approach: classes were stratified based on current academic grades and field of study, and students from conveniently selected classes were sampled during class break times (20 min). The research team explained the purpose of the study to potential participants, emphasizing voluntary participation, confidentiality of the data, and the option to withdraw from the survey at any time. Completing the questionnaire take between 10 and 15 min.

The sample involved participants from 18 departments. A total of 950 questionnaires were distributed, with 846 valid responses received (an effective response rate of 89%). Of these, 317 were men (37.5%), and 529 were women (62.5%). The distribution by academic year was as follows: 257 first-year students (30.4%), 411 second-year students (48.6%), 150 third-year students (17.7%), and 28 fourth-year students (3.3%). Of these the respondents, 442 students (52.2%) were from humanities, social sciences, and arts disciplines, while 404 students (47.8%) were from science and medical disciplines. The study was approved by the Ethics Committee of Soochow University [SUDA20210420H02].

Questionnaire

University Students’ Academic Development Satisfaction Scale

The “university students’ academic development satisfaction scale” measures students’ satisfaction with their own academic development in higher education. The design of the scale uses the academic self-concept theory (Marsh, 1992; Marsh et al., 2008, 2009, 2018) as a reference. Marsh et al. (2008) divided academic self-concept (ASC) into two categories: (a) students’ self-perception in specific disciplines, such as math or science self-concept, and (b) more general academic areas, known as global or general ASC. The latter encompasses students’ perceptions of their academic achievements, their sense of academic competence, their expectations for academic success or failure, and their academic self-beliefs. Adapting the scale developed by Guo et al. (2011), students’ satisfaction with their own academic development is divided into four dimensions: (a) academic ability (such as academic grades, specialized capabilities, and academic achievements), (b) academic experience (sense of belonging in classes, classroom participation, and achievement in learning), (c) academic behavior (self-discipline in studying, level of commitment, ability to execute tasks, and perseverance), and (d) academic value (including the significance of university studies for future job applications, skill enhancement, and cognitive expansion). Consisting of 17 items, each item is scored on a scale from 1 (completely dissatisfied) to 5 (completely satisfied).

University Students’ Interpersonal Relationship Scale

We adapted the interpersonal relationship scale from Lai et al. (2013) to examine students’ interactions with four groups: peers (e.g., “I rarely argue with my peers”), teachers (e.g., “I feel comfortable talking to my teachers”), family (e.g., “I can freely express my thoughts and feelings with my family”), and online acquaintances (e.g., “I am rarely attacked or harmed online”). The scale evaluates the frequency of interaction, and level of rapport, comfort, freedom of expression in interactions. It consists of 23 items across four dimensions: peer relationships, teacher–student relationships, parent–child relationships, and online relationships. Each item is scored on a scale from 1 (strongly disagree) to 5 (strongly agree).

University Students’ Social Support Questionnaire

The literature on social support differentiates between support that is perceived and that which the student actually receives (Haber et al., 2007). This study focuses on perceived social support. The categorization of perceived social support varies among scholars. For example, Cohen et al. (1985) not only sorted perceived support by its source, such as from partners, parents, peers, and other family members, but also by its nature—delineating between companionship, emotional support, and instrumental support. Xiao and Yang (1987, as cited in Song & Fan, 2013) categorized perceived social support into subjective support, objective support, and support utilization. Chu (2010) differentiated social support into tangible support and emotional support. There are numerous ways to categorize social support, but to comprehensively capture the types of social support, we first divided the perceived social support among university students into three categories: academic support, non-academic support, and support utilization (referring to the proactivity of students seeking support when in need during their college years). Under these three aspects, based on two types of support (tangible support in the forms of financial aid, material goods, physical actions, advice, information, experience, and emotional support in the forms of comfort, encouragement, praise, and care), we formulated six items. In terms of academic and non-academic areas, a total of four questions relates to tangible and emotional support. We scored the intensity of support (none, little, average, a lot) accordingly with points (0, 1, 2, 3) and accumulated the points based on the source of support (family, teachers, friends, roommates, classmates, partners, faculty, school, and others). For the two items regarding the utilization of tangible and emotional support, the proactivity of seeking support is scored (will not seek support; will only seek support from one or two close individuals; will seek support when people who are not close to you if they proactively inquire; actively seeks support from as many people as possible) with points (0, 1, 2, 3).

Data Analysis

Most statistics used in SEM assume that the multivariate distribution is normally distributed because non-normality will affect the accuracy of parameter estimates and fitness of model (Jobst et al., 2022). While a normal distribution is ideal, it is not common in reality; hence, a certain degree of non-normality is acceptable in research, usually expressed as values for the kurtosis and skewness of the observed variable between −2.0 and +3.5 (Lei & Lomax, 2005). The values of kurtosis and skewness of observed variables in SEM and the item data in the questionnaire are all between −1.18 and +1.03, and the non-normality of the sample data can be accepted.

The following data analyses were performed using SPSS 26.0 software: (a) Harman’s single-factor test to measure the effects of common method variance; (b) internal consistency to assess the reliability of the questionnaire; (c) descriptive statistics to present the mean and standard deviation of the variables; and (d) correlation analysis to measure the relationships between three variables, setting a preliminary foundation for subsequent SEM analysis, as proposed by Baron and Kenny (1986). In addition, the following analyses were executed using Amos 21.0 software: (a) confirmatory factor analysis to measure the construct validity of the questionnaire; (b) SEM to teste the hypotheses; and (c) the Bootstrap method to test the mediation effect. The data was processed in the Amos 21.0 software using SEM. SEM is a statistical approach that examines the relationships among variables and structures among them, encompassing ideas and methods from factor analysis, path analysis, and multiple linear regression (Sun, 2007).

Cronbach’s alpha coefficient is used to reflect the internal consistency of the questionnaires or scales. A value of alpha ≥.70 is defined as “acceptable” and an alpha ≥.80 as “good” (da Silva & Padula, 2021). For the confirmatory factor analysis and SEM analysis, the following indices are employed to evaluate model fit: χ²/df, goodness of fit index (GFI), the root mean square error of approximation (RMSEA), the Tucker–Lewis Index (TLI), and the Comparative Fit Index (CFI). Model fit was assessed based on the standard threshold criteria: χ²/df < 5, GFI > 0.9, RMSEA < 0.08, TLI > 0.9, and CFI > 0.9 (Y. Li et al., 2023).

Reliability and Validity Tests

The results from the Harman single-factor analysis indicated that the first common factor accounted for 19.0% of the variance, which is significantly below the threshold value of 50% (Cowin et al., 2013). This demonstrates that common method deviation will not influence data analysis results (Qian et al., 2023).

Considering only six items in the university students’ social support questionnaire, it is difficult to divide into several dimensions. Therefore, we take the six items as one dimension for reliability and validity test. As shown in Table 1, a questionnaire and two scales used in this study display satisfactory Cronbach’s alpha coefficients and exhibit suitable model fit indexes, signifying their good reliability and validity.

Table 1.

The Results of Reliability and Validity Tests.

Questionnaire/scale	Overall Cronbach’s alpha coefficient	Cronbach’s alpha coefficient for each dimension (number of items)	χ²/df	GFI	RMSEA	TLI	CFI
University students’ interpersonal relationship	0.89	0.86 (7); 0.88 (5); 0.90 (7); 0.83 (4)	4.62	0.90	0.07	0.91	0.92
University students’ academic development satisfaction	0.93	0.89 (5); 0.76 (4); 0.89 (5); 0.85 (3)	3.80	0.94	0.06	0.95	0.96
University students’ social support questionnaire	0.71	—	3.87	0.99	0.06	0.98	0.99

Results

Descriptive Statistics and Correlation Analysis

Table 2 shows that university students exhibit a relatively low satisfaction level regarding their academic development, and the score of the academic experience dimension is the lowest. Excluding a few specific instances related to online relationships, all variables show a significant correlation at .001 level (two-tailed).

Table 2.

The Results of Descriptive Statistics and Correlation Analysis.

Variable	M	SD	1	2	3	4	5	6	7	8
1. Academic ability satisfaction	3.02	0.70
2. Academic experience satisfaction	2.94	0.69	.58***
3. Academic behavior satisfaction	3.00	0.79	.67***	.55***
4. Academic value satisfaction	3.19	0.82	.63***	.59***	.67***
5. Peer relationships	4.02	0.61	.20***	.22***	.18***	.17***
6. Teacher–student relationships	3.21	0.79	.32***	.40***	.33***	.36***	.41***
7. Parent–child relationships	4.17	0.68	.21***	.19***	.24***	.24***	.50***	.35***
8. Online relationships	3.05	0.94	0.05	0.09**	0.05	0.07*	0.16***	0.23***	0.08*
9. Social support	1.58	0.40	.27***	.36***	.28***	.27***	.30***	.36***	.28***	.07*

Note. The other values are the Pearson correlation coefficients; *p < .05, **p < .01, ***p < .001. M = arithmetic mean; SD = standard deviation.

Results of SEM Analysis

To test the research hypothesis, Model M₁ was initially built to probe the influence of interpersonal relationships on academic development satisfaction (H₁). Subsequently, Model M₂ added social support based on Model 1, examining the mediating effect of social support on the relationship between interpersonal relationships and academic development satisfaction (H₂). The data was analyzed using the maximum likelihood estimation method and the Bootstrap method with 5,000 resamples and a 95% confidence interval (CI). Interpersonal relationships and academic development satisfaction are the latent variables in M₁ and M₂, and social support is a manifest variable in M₂. Figure 1 shows the standard solution of the two models (M₁ and M₂).

Figure 1.

The standard solution of the two models (M₁ and M₂).

According to Figure 1, interpersonal relationships have a significant positive influence (β = .52; p < .001) on university students’ academic development satisfaction. After adding the mediating variable of social support, the standardized regression coefficient between the interpersonal relationships and academic development satisfaction decreases from 0.52 to 0.47, and the explained variance (R²) for academic development satisfaction increases from 0.27 to 0.31. Concurrently, the standardized regression coefficients between interpersonal relationships and social support, and between social support and academic development satisfaction are 0.48 and 0.14, respectively. The significance of all standardized regression coefficients remains at the 0.001 level in M₁ and M₂. These findings indicate that social support acts as a partial mediator between interpersonal relationships and academic development satisfaction. Table 3 presents the fit indices for both Models M₁ and M₂. All fit indexes fall within acceptable standards. It suggests that both constructed models (M₁ and M₂) align well with the observed data and have a goodness of fit. Thus, H₁ and H₂ are validated.

Table 3.

Fit Indices of the Models.

Model	GFI	RMSEA	TLI	CFI	χ²/df
M₂	0.98	0.06	0.96	0.97	3.62
M₁	0.98	0.05	0.97	0.98	3.22

Test of the Mediating Effects

Wen et al. (2016) suggested that, to compensate for the limitations of mere significance (non-zero hypotheses), it is crucial to include effect sizes in the results of statistical analyses. When evaluating mediation effects, the mediation effect size—defined as the ratio of the mediating effect to the total effect—is often employed due to its robustness across various measurement units and sample sizes, as well as its monotonic, non-negative, bounded, and standardized properties. As illustrated in Table 4, the indirect effect, representing the mediating role of social support, is estimated at 0.07, with a 95% Bootstrap CI of [0.02, 0.10] that excludes zero. Both the direct and total effects are measured at 0.48 and 0.54, respectively, and their 95% Bootstrap CIs also exclude zero. All effects are significant at the .001 level. Furthermore, the mediation effect size can be calculated as 12.96% (derived from the proportion of 0.07 to 0.54), reinforcing the notion that social support serves as a partial mediator between university students’ interpersonal relationships and their academic development satisfaction.

Table 4.

The Values of Direct Effect, Mediating Effect, and Total Effect.

Effect type	Effect value	Standard error	Significance	95% CI
Effect type	Effect value	Standard error	Significance	Lower limit	Upper limit
Total effect	0.54	0.04	.000	0.46	0.63
Direct effect	0.48	0.05	.000	0.37	0.58
Indirect effect	0.07	0.02	.000	0.02	0.10

Discussion and conclusion

This study, from the perspective of college impact theory, aimed to investigate the relationship between interpersonal relationships and university students’ academic development satisfaction, incorporating social support as a mediating variable to elucidate the relationship between the two constructs further. The results demonstrate a significant positive correlation between interpersonal relationships, social support, and the academic development satisfaction of university students. Furthermore, interpersonal relationships were found to exert both direct and indirect effects on students’ academic development satisfaction, with social support serving as a partial mediator.

Relationship Between Interpersonal Relationships and University Students’ Satisfaction With Their Academic Development

Consistent with the findings of this study, prior researchers (Aldemir & Gülcan, 2004; Kim & Sax, 2014; Kuo et al., 2014; Siming et al., 2015) have also demonstrated that interpersonal relationships significantly influence students’ satisfaction with the institution and their self-perceived academic performance. This influence may stem from the ways in which students’ perceptions and expectations of academic development evolve through social interactions. As highlighted by Pascarella (1980), informal student–faculty interactions play a crucial role in shaping students’ attitudes and values. Drawing on Rossi’s (1966) concept of the “interpersonal environment,” which exposes individuals to the values, attitudes, and behaviors of significant others, such interactions can diminish differences between students and faculty as students internalize academic norms and outlooks. This process of alignment may reduce psychosocial stress within their interpersonal environments, subsequently enhancing their academic performance. Therefore, the active interaction of students with the surrounding groups not only promotes their own academic development but also influences their attitudes towards it.

Furthermore, the “big-fish-little-pond effect” from academic self-concept (ASC) theory (J. Fang et al., 2018; Marsh et al., 2008; Payne et al., 2020) indicates that students’ self-evaluation of academic development is shaped not only by their academic performance but also by social comparisons of their performance with peers within interpersonal contexts. This underscores the significant impact of interpersonal relationships on students’ academic development satisfaction.

The Mediating Effect of Social Support on Interpersonal Relationships and University Students’ Satisfaction With Their Academic Development

Additionally, the findings reveal a significant mediating effect of social support in the relationship between interpersonal relationships and academic development satisfaction. This result carries two important implications.

First, social support exerts a substantial influence on university students’ satisfaction with their academic development. This aligns with prior research by DeGarmo and Martinez (2006), Gilbert et al. (2017), and Zalazar-Jaime et al. (2021), which consistently reported that students receiving greater social support exhibit higher levels of academic satisfaction compared to those with less support. Overall, the impact of social support on university students’ satisfaction with their academic development can be explained through two principal mechanisms: stress buffering and resource enhancement. The latter becomes prevalent under stressful situations, while the former becomes more prominent in stressful contexts (He et al., 2015). Under ordinary circumstances, social support functions as a form of social capital for university students. It provides tangible resources—such as financial assistance, advice, and information—which help create a supportive social environment conducive to students’ academic success, thereby indirectly enhancing their satisfaction with academic development. Mishra (2020) further corroborated that family-based social capital, encompassing informational, motivational, and expectational support, significantly contributes to students’ academic achievement. In times of stress, social support serves as psychological capital by offering emotional comfort and encouragement, which can foster a sense of psychological well-being, reduce anxiety, and help students restore their usual academic performance (Y. J. Li et al., 2014; Wilcox et al., 2005; Wu, 2013).

Second, interpersonal relationships play a critical role in shaping the perceived social support among university students. This finding is supported by Eggens et al. (2007), who proposed that interpersonal relationships and social support are closely interwoven concepts. Similarly, Gutiérrez et al. (2017), Jaggars and Xu (2016), and Rodríguez et al. (2012) found that both the quantity and quality of interpersonal relationships significantly influence perceived social support. While Eggens et al. (2007) contend that social support derives exclusively from interpersonal relationships, this perspective does not fully capture the context in China. Beyond a student’s immediate social network, entities such as the Chinese government and institutional bodies also serve as salient sources of social support—for instance, through financial grant programs (Huang et al., 2017). These programs provide substantial economic assistance to students from disadvantaged backgrounds, covering tuition and partial living expenses. Consequently, although the government and institutions may not form part of students’ interpersonal networks, they nevertheless constitute an important source of social support within the Chinese socio-institutional context. Therefore, social support serves as a mediator in the relationship between interpersonal relationships and university students’ satisfaction with academic development.

However, the mediating effect of social support was found to be less pronounced than initially hypothesized. This relatively limited influence may be attributed to the cultural predisposition among Chinese university students, who often emphasize individual effort over actively seeking help from others when pursuing goals. The descriptive statistics for the support utilization items in this study reveal a consistent pattern: when confronted with challenges, more than half of respondents reported relying on only one or two close individuals for tangible (55.8%) or emotional (66.8%) support. This tendency aligns with the observations of Chi et al. (2016), who highlighted the strong cultural emphasis on diligence, persistence, and self-discipline within Chinese universities. In essence, Chinese students tend to prioritize self-reliance and personal determination, frequently overcoming challenges through their own perseverance rather than seeking external support, thereby attenuating the mediating role of social support.

Contributions and Implications

This study offers several theoretical contributions and practical implications. Firstly, while existing research on college student satisfaction has primarily focused on institutional facilities and services—such as academic programs, teaching quality, campus facilities, student support, and teacher-student interaction (Wong & Chapman, 2023)—few studies have specifically addressed students’ own academic goals or expectations. This study contributes to the literature by introducing and empirically examining the concept of academic development satisfaction, thereby providing a necessary complement to conventional approaches to student satisfaction. Secondly, although college impact theory acknowledges the role of environmental factors in shaping student outcomes, it often describes these effects in broad, general terms (Shi, 2011). This study deepens the theoretical discourse by incorporating social support as a mediator to try to explain the mechanism of interpersonal relationships as an environmental factor influencing academic development satisfaction as an outcome factor. Therefore, the findings refine and extend college impact theory by elucidating a specific psychosocial pathway. Finally, the identified mediating role of social support suggests practical value for higher education institutions. By offering structured guidance and support programs, universities can help students cultivate robust interpersonal networks, which in turn may enhance their academic development satisfaction. Such interventions could reduce dropout rates resulting from unmet students’ academic needs and promote the sustainable development of both students and higher education institutions.

Limitations and Future Directions

Although this study has generated some valuable findings, it also has some limitations, which can be used to guide future research. First, while the participant distribution is relatively balanced in terms of grade, discipline, and gender, they all come from a public research university in Eastern China. Future studies should include students from different types of universities and various countries to confirm the generalizability of the results. Second, we initially attempted to segment social support into various variables and construct multiple mediating models. However, in our effort to comprehensively cover the types and sources of social support perceived by students, the questionnaire we used was not a form of highly structured scales. As a result, the fit indicators of the multi-mediation model are not up to the acceptable standard. In the following studies, we suggest using a more structured scale to collect data for multi-mediating model analysis and explore the mediating influence of different types of social support. Lastly, all variables in the study are assessed through self-reported measures, which could lead to potential biases associated with social desirability. Future research should consider adopting more objective and comprehensive assessment methods. Despite these limitations, our study is the first to specifically explore the university students’ academic development satisfaction and its interaction with other determinants.

Footnotes

Acknowledgements

The authors wish to thank all the students for their participation in this study.

ORCID iDs

Yan Song

Qingyu Xu

Di Hu

Ethical Considerations

This study was approved by the Ethics Committee of Soochow University [SUDA20210420H02].

Consent to Participate

All participants were verbally informed consent, and their personal information did not appear in this article.

Author Contributions

Yan Song conceived the original idea for the study, conducted data collection and analysis, and wrote and revised the manuscript. Lingying Zhao assisted in the conceptualization of the study and revision of the manuscript. Qingyu Xu contributed to the study’s conception and design, supervised the writing of the manuscript and its revision. Di Hu contributed constructive suggestions on the research topic and analysis framework, participated in drafting and revising the manuscript, and reviewed the final revised version.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets used or produced in this study can be obtained from the corresponding author upon reasonable request.

References

Aldemir

Gülcan

(2004). Student satisfaction in higher education: A Turkish case. Higher Education Management and Policy, 16(2), 109–122. https://doi.org/10.1787/hemp-v16-art19-en

Arnett

J. J.

(2015). Emerging adulthood: The winding road from the late teens through the twenties (2nd ed.). Oxford University Press.

Astin

A. W.

(1970a). The methodology of research on college impact, part one. Sociology of Education, 42(3), 223–254. https://files.eric.ed.gov/fulltext/ED041948.pdf

Astin

A. W.

(1970b). The methodology of research on college impact, part two. Sociology of Education, 43(4), 437–450. https://doi.org/10.2307/2111842

Baker-Doyle

K. J.

(2012). First-year teachers’ support networks: Intentional professional networks and diverse professional allies. The New Educator, 8(1), 65–85. https://doi.org/10.1080/1547688x.2012.641870

Bao

(2014). 未完成的转型:高等教育影响力与学生发展 [Unfinished Transformation: Higher education Impact and Student Development]. Educational Science Publishing House.

Baron

R. M.

Kenny

D. A.

(1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

Berkman

L. F.

Glass

Brissette

Seeman

T. E.

(2000). From social integration to health: Durkheim in the new millennium. Social Science & Medicine, 51(6), 843–857. https://doi.org/10.1016/s0277-9536(00)00065-4

Cabrera

A. F.

Nora

Castañeda

M. B.

(1993). College persistence: Structural equations modeling test of an integrated model of student retention. The Journal of Higher Education, 64(2), 123–139. https://doi.org/10.1080/00221546.1993.11778419

10.

Cant

Gazula

Ryan

(2023). Predictors of nursing student satisfaction as a key quality indicator of tertiary students’ education experience: An integrative review. Nurse Education Today,126, Article 105806. https://doi.org/10.1016/j.nedt.2023.105806

11.

Chang

S. H.

Smith

R. A.

(2008). Effectiveness of personal interaction in a learner-centered paradigm distance education class based on student satisfaction. Journal of Research on Technology in Education, 40(4), 407–426. https://doi.org/10.1080/15391523.2008.10782514

12.

Chi

Liu

Bai

(2016). College environment, student involvement, and intellectual development: Evidence in China. Higher Education, 74(1), 81–99. https://doi.org/10.1007/s10734-016-0030-z

13.

Chu

R. J.

(2010). How family support and Internet self-efficacy influence the effects of e-learning among higher aged adults – Analyses of gender and age differences. Computers & Education, 55(1), 255–264. https://doi.org/10.1016/j.compedu.2010.01.011

14.

Cohen

Mermelstein

Kamarck

Hoberman

H. M.

(1985). Measuring the functional components of social support. In Sarason

I. G.

Sarason

B. R.

(Eds.), Social support: Theory, research and applications (pp. 73–94). Springer. https://doi.org/10.1007/978-94-009-5115-0_5

15.

Cowin

L. S.

Johnson

Wilson

Borgese

(2013). The psychometric properties of five Professional Identity measures in a sample of nursing students. Nurse Education Today, 33(6), 608–613. https://doi.org/10.1016/j.nedt.2012.07.008

16.

Danielsen

A. G.

Samdal

Hetland

Wold

(2009). School-related social support and students’ perceived life satisfaction. The Journal of Educational Research, 102(4), 303–320. https://doi.org/10.3200/joer.102.4.303-320

17.

da Silva

A. M.

Padula

R. S

. (2021). Factor structure and short version of the modified Fresno test to assess the use of the evidence-based practice in physiotherapists. BMC Medical Education, 21(1), Article 135. https://doi.org/10.1186/s12909-021-02535-9

18.

DeGarmo

D. S.

Martinez

C. R.

Jr. (2006). A culturally informed model of academic well-being for Latino youth: The importance of discriminatory experiences and social support. Family Relations, 55(3), 267–278. https://doi.org/10.1111/j.1741-3729.2006.00401.x

19.

Douglas

Barnes

(2006). Measuring student satisfaction at a UK university. Quality Assurance in Education, 14(3), 251–267. https://doi.org/10.1108/09684880610678568

20.

Duque

(2014). A framework for analysing higher education performance: Students’ satisfaction, perceived learning outcomes, and dropout intentions. Total Quality Management & Business Excellence, 25(1–2), 1–21. https://doi.org/10.1080/14783363.2013.807677

21.

Eather

Mavilidi

M. F.

Sharp

Parkes

(2022). Programmes targeting student retention/success and satisfaction/experience in higher education: A systematic review. Journal of Higher Education Policy and Management, 44(3), 223–239. https://doi.org/10.1080/1360080X.2021.2021600

22.

Eggens

van der Werf

M. P. C.

Bosker

R. J.

(2007). The influence of personal networks and social support on study attainment of students in university education. Higher Education, 55(5), 553–573. https://doi.org/10.1007/s10734-007-9074-4

23.

Fang

Huang

Zhang

Huang

Yuan

(2018). The Big-Fish-Little-Pond effect on academic self-concept: A meta-analysis. Frontiers in Psychology, 9, Article 1569. https://doi.org/10.3389/fpsyg.2018.01569

24.

Fang

Sun

R. C.

Yuen

(2016). Acculturation, economic stress, social relationships and school satisfaction among migrant children in urban China. Journal of Happiness Studies, 17(2), 507–531. https://doi.org/10.1007/s10902-014-9604-6

25.

Gilbert

L. R.

Spears Brown

Mistry

R. S.

(2017). Latino immigrant parents’ financial stress, depression, and academic involvement predicting child academic success. Psychology in the Schools, 54(9), 1202–1215. https://doi.org/10.1002/pits.22067

26.

Granovetter

M. S.

(1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. https://doi.org/10.1086/225469

27.

Guo

Zhao

X. Y.

Zhang

D. J.

(2011). The compiling of adolescent students’ general academic self-concept. Journal of Southwest University (Natural Science Edition), 33(12), 155–161.https://doi.org/10.13718/j.cnki.xdzk.2011.12.021

28.

Gutiérrez

Tomás

J.-M.

Romero

Barrica

J.M.

(2017). Perceived social support, school engagement and satisfaction with school. Revista de Psicodidáctica (English Ed.), 22(2), 111–117. https://doi.org/10.1016/j.psicoe.2017.05.001

29.

Haber

M. G.

Cohen

J. L.

Lucas

Baltes

B. B.

(2007). The relationship between self-reported received and perceived social support: A meta-analytic review. American Journal of Community Psychology, 39(1–2), 133–144. https://doi.org/10.1007/s10464-007-9100-9

30.

A. M.

Hui

Q. P.

Liu

H. S.

(2015). Relationship between social support and loneliness in undergraduates: The mediating role of gratitude. Chinese Journal of Clinical Psychology, 23(1), 150–153. https://doi.org/10.16128/j.cnki.1005-3611.2015.01.034

31.

House

J. S.

Umberson

Landis

K. R.

(1988). Structures and processes of social support. Annual Review of Sociology, 14(1), 293–318. https://doi.org/10.1146/annurev.so.14.080188.001453

32.

Hsu

J. Y.

Chen

C. C.

Ting

P. F.

(2018). Understanding MOOC continuance: An empirical examination of social support theory. Interactive Learning Environments, 26(8), 1100–1118. https://doi.org/10.1080/10494820.2018.1446990

33.

Huang

Liao

(2017). More money, better performance? The effects of student loans and need-based grants in China’s higher education. China Economic Review, 51, 208–227. https://doi.org/10.1016/j.chieco.2017.09.005

34.

Jaggars

S. S.

(2016). How do online course design features influence student performance? Computers & Education, 95, 270–284. https://doi.org/10.1016/j.compedu.2016.01.014

35.

Jobst

L. J.

Auerswald

Moshagen

(2022). The effect of latent and error non-normality on measures of fit in structural equation modeling. Educational and Psychological Measurement, 82(5), 911-937. https://doi.org/10.1177/00131644211046201

36.

Johnson

Z. S.

Cascio

Massiah

C. A.

(2014). Explaining student interaction and satisfaction: An empirical investigation of delivery mode influence. Marketing Education Review, 24(3), 227–238. https://doi.org/10.2753/MER1052-8008240304

37.

Kim

Y. K.

Sax

L. J.

(2014). The effects of student–faculty interaction on academic self-concept: Does academic major matter? Research in Higher Education, 55(8), 780–809. https://doi.org/10.1007/s11162-014-9335-x

38.

Kuo

Y.-C.

Walker

A. E.

Schroder

K. E.

Belland

B. R.

(2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35–50. https://doi.org/10.1016/j.iheduc.2013.10.001

39.

Lai

C. H.

Gwung

H. L.

(2013). The effect of gender and Internet usage on physical and cyber interpersonal relationships. Computers & Education, 69, 303–309. https://doi.org/10.1016/j.compedu.2013.07.009

40.

Lei

Lomax

R. G.

(2005). The effect of varying degrees of nonnormality in structural equation modeling. Structural Equation Modeling, 12(1), 1–27. https://doi.org/10.1207/s15328007sem1201_1

41.

Lent

R. W.

Singley

Sheu

H.-B.

Schmidt

J. A.

Schmidt

L. C.

(2007). Relation of social-cognitive factors to academic satisfaction in engineering students. Journal of Career Assessment, 15(1), 87–97. https://doi.org/10.1177/1069072706294518

42.

Dong

Tang

Guo

Chen

(2023). The effect of parenting styles on Chinese undergraduate nursing students’ academic procrastination: The mediating role of causal attribution and self-efficacy. Frontiers in Psychology, 14, Article 1167660. https://doi.org/10.3389/fpsyg.2023.1167660

43.

Y. J.

Zhu

X. S.

Chen

Y. H.

(2014). The relationship of college students' social support and well-being: The mediating role of social self-esteem. Studies of Psychology and Behavior, 12(3), 351–356.

44.

Liou

Y. H.

Canrinus

E. T.

(2020). A capital framework for professional learning and practice. International Journal of Educational Research, 100, Article 101527. https://doi.org/10.1016/j.ijer.2019.101527

45.

Marsh

H. W.

(1992). Content specificity of relations between academic achievement and academic self-concept. Journal of Educational Psychology, 84(1), 35–42. https://doi.org/10.1037/0022-0663.84.1.35

46.

Marsh

H. W.

Lüdtke

Trautwein

Morin

A. J. S.

(2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person- and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling: A Multidisciplinary Journal, 16(2), 191–225. https://doi.org/10.1080/10705510902751010

47.

Marsh

H. W.

Pekrun

Murayama

Arens

K. A.

Parker

P. D.

Guo

Dicke

(2018). An integrated model of academic self-concept development: academic self-concept, grades, test scores, and tracking over six years. Developmental Psychology, 54(2), 263–280. https://doi.org/10.1037/dev0000393

48.

Marsh

H. W.

Seaton

Trautwein

Lüdtke

Hau

K. T.

O’Mara

A. J.

Craven

R. G.

(2008). The big-fish–little-pond-effect stands up to critical scrutiny: Implications for theory, methodology, and future research. Educational Psychology Review, 20(3), 319–350. https://doi.org/10.1007/s10648-008-9075-6

49.

Mishra

(2020). Social networks, social capital, social support and academic success in higher education: A systematic review with a special focus on “underrepresented” students. Educational Research Review, 29, Article 100307. https://doi.org/10.1016/j.edurev.2019.100307

50.

Moltesen

Bjørn

(2018). LCA and sustainability. In Hauschild

Rosenbaum

Olsen

(Eds.), Life cycle assessment (pp. 43–55). Springer. https://doi.org/10.1007/978-3-319-56475-3_5

51.

Nastasić

Banjević

Gardašević

(2019). Student satisfaction as a performance indicator of higher education institution. Mednarodno Inovativno Poslovanje, 11(2), 67–76. https://doi.org/10.32015/JIBM/2019-11-2-8

52.

Olawumi

T. O.

Chan

D. W. M.

(2018). A scientometric review of global research on sustainability and sustainable development. Journal of Cleaner Production, 183, 231–250. https://doi.org/10.1016/j.jclepro.2018.02.162

53.

Oliver

R. L.

DeSarbo

W. S.

(1988). Response determinants in satisfaction judgments. Journal of consumer research, 14(4), 495–507. https://doi.org/10.1086/209131

54.

Pascarella

E. T.

(1980). Student-faculty informal contact and college outcomes. Review of Educational Research, 50(4), 545–595. https://doi.org/10.3102/00346543050004545

55.

Pascarella

E. T.

(1985). College environmental influences on learning and cognitive development: A critical review and synthesis. In Smart

J. C.

(Ed.), Higher education: Handbook of theory and research (pp. 1–61). Agathon. https://www.sci-hub.se/10.1007/978-94-007-0702-3

56.

Pascarella

E. T.

Pierson

C. T.

Wolniak

G. C.

Terenzini

P. T.

(2004). First-generation college students: additional evidence on college experiences and outcomes. The Journal of Higher Education, 75(3), 249–284. https://doi.org/10.1080/00221546.2004.11772256

57.

Pascarella

E. T.

Terenzini

P. T.

(1980). Predicting freshman persistence and voluntary dropout decisions from a theoretical model. The Journal of Higher Education, 51(1), 60–75. https://doi.org/10.1080/00221546.1980.11780030

58.

Payne

A. A.

Smith

(2020). Big Fish, Small Pond: The effect of rank at entry on postsecondary outcomes. Southern Economic Journal, 86(4), 1475–1509. https://doi.org/10.1002/soej.12420

59.

Pedro

Alves

Leitão

(2023). Sustainable development practices in public higher education: A new conceptual framework for nurturing student satisfaction and reinforcing attractiveness to international students. Sustainable Development, 32, 2565–2582. https://doi.org/10.1002/sd.2798

60.

Qian

Liu

Zhang

(2023). Direct and indirect effects of self-directed learning on creativity in healthcare undergraduates: A chain mediation model of openness to challenge and diversity and creative self-efficacy. Frontiers in Psychology, 14, Article 1182692. https://doi.org/10.3389/fpsyg.2023.1182692

61.

Ramos-Díaz

Rodríguez-Fernández

Fernández-Zabala

Revuelta

Zuazagoitia

(2016). Adolescent students’ perceived social support, self-concept and school engagement. Revista de Psicodidáctica, 21(2), 339–356. https://doi.org/10.1387/RevPsicodidact.14848

62.

Raza

Ali

Rafiq

Xing

Asif

Jing

(2023). Comparison of higher education in Pakistan and China: A sustainable development in student’s perspective. Sustainability, 15(5), Article 4327. https://doi.org/10.3390/su15054327

63.

Rodríguez

Droguett

Revuelta

(2012). School and personal adjustment in adolescence: The role of academic self-concept and perceived social support. Revista de Psicodidáctica, 17(2), 397–414. https://doi.org/10.1387/Rev.Psicodidact.3002

64.

Rossi

P. H.

(1966). Research strategies in measuring peer group influence. In Newcomb

T. M.

Wilson

E. K.

(Eds.), College peer groups (pp. 190–214). Aldine.

65.

Rossini

Bulfone

Vellone

Alvaro

(2021). Nursing students’ satisfaction with the curriculum: An integrative review. Journal of Professional Nursing, 37(3), 648–661. https://doi.org/10.1016/j.profnurs.2021.02.003

66.

Shi

W. L.

(2011). A review of college impact theory of student development change. Education Research Monthly, 7, 8–11. https://doi.org/10.16477/j.cnki.issn1674-2311.2011.07.017

67.

Siming

Niamatullah, Gao

Shaf

(2015). Factors leading to students’ satisfaction in the higher learning institutions. Journal of Education and Practice, 6(31), 114–118.

68.

Sivandani

Koohbanani

S. E.

Vahidi

(2013). The relation between social support and self-efficacy with academic achievement and school satisfaction among female junior high school students in Birjand. Procedia - Social and Behavioral Sciences, 84, 668–673. https://doi.org/10.1016/j.sbspro.2013.06.623

69.

Song

Fan

(2013). A meta-analysis of the relationship between social support and subjective well-being. Advances in Psychological Science, 21(8), 1357–1370. https://doi.org/10.3724/SP.J.1042.2013.01357

70.

Spady

W. G.

(1970). Dropouts from higher education: An interdisciplinary review and synthesis. Interchange, 1(1), 64–85. https://doi.org/10.1007/bf02214313

71.

Sun

(2007). Structural equation model for subjective well-being. Statistical Research, 2, 27–32. https://doi.org/10.19343/j.cnki.11-1302/c.2007.02.004

72.

Tinto

(1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. https://doi.org/10.3102/00346543045001089

73.

van Rooij

Jansen

van de Grift

. (2018). First-year university students’ academic success: The importance of academic adjustment. European Journal of Psychology of Education, 33(4), 749–767. https://doi.org/10.1007/s10212-017-0347-8

74.

Walker

Rossi

Anastasi

Gray-Ganter

Tennent

(2016). Indicators of undergraduate nursing students’ satisfaction with their learning journey: An integrative review. Nurse Education Today, 43, 40–48. https://doi.org/10.1016/j.nedt.2016.04.011

75.

Weidman

(1989). Undergraduate socialization: A conceptual approach. In Smart

J. C.

(Ed.), Higher education: Handbook of theory and research (pp. 289–322). Agathon. https://www.researchgate.net/publication/256475579_Undergraduate_socialization_A_conceptual_approach

76.

Wen

Z. L.

Fan

X. T.

B. J.

Chen

Y. S.

(2016). Characteristics of an effect size and appropriateness of mediation effect size measures revisited. Acta Psychologica Sinica, 48(4), 435–443. https://doi.org/10.3724/SP.J.1041.2016.00435

77.

Wentzel

K. R.

(1999). Social-motivational processes and interpersonal relationships: Implications for understanding motivation at school. Journal of Educational Psychology, 91(1), 76–97. https://doi.org/10.1037/0022-0663.91.1.76

78.

Wilcox

Winn

Fyvie-Gauld

(2005). “It was nothing to do with the university, it was just the people”: The role of social support in the first-year experience of higher education. Studies in Higher Education, 30(6), 707–722. https://doi.org/10.1080/03075070500340036

79.

Wong

W.H.

Chapman

(2023). Student satisfaction and interaction in higher education. High Education, 85(5), 957–978. https://doi.org/10.1007/s10734-022-00874-0

80.

S.S.

(2013). The relationship between perceived social support, psychological capital and academic achievement of college students [Master’s dissertation, Shandong Normal University]. CNKI.

81.

Zhu

Wang

Qian

Shi

(2023). Psychological distress and academic self-efficacy of nursing undergraduates under the normalization of COVID-19: Multiple mediating roles of social support and mindfulness. BMC Medical Education, 23, Article 348. https://doi.org/10.1186/s12909-023-04288-z

82.

Yamada

Klugar

Ivanova

Oborna

(2014). Psychological distress and academic self-perception among international medical students: The role of peer social support. BMC Medical Education, 14, Article 256. https://doi.org/10.1186/s12909-014-0256-3

83.

Zalazar-Jaime

M. F.

Moretti

L. S.

García-Batista

Z. E.

Medrano

L. A.

(2021). Evaluation of an academic satisfaction model in E-learning education contexts. Interactive Learning Environments, 31(7), 4687–4697. https://doi.org/10.1080/10494820.2021.1979047

84.

Zhao

F. F.

Wang

Y. H.

Ping

(2023). Work volition and academic satisfaction in Chinese female college students: the moderating role of incremental theory of work volition. International Journal for Educational and Vocational Guidance, 23(3), 599–614. https://doi.org/10.1007/s10775-022-09542-2

85.

Zhao

Zhu

Cai

Zhang

(2023). Improving sustainability of learning outcomes: An empirical study of medical students’ autonomous learning. Sustainability, 15, Article 5668. https://doi.org/10.3390/su15075668

86.

Zimet

G. D.

Dahlem

N. W.

Zimet

S. G.

Farley

G. K.

(1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 52(1), 30–41. https://doi.org/10.1207/s15327752jpa5201_2

Improving University Students’ Academic Development Satisfaction Through Interpersonal Relationship: Social Support as a Mediator

Abstract

Plain Language Summary

Keywords

Introduction

Relevant Theory, Literature Review, and Research Hypotheses

Student Satisfaction and Interpersonal Relationships

Social Support as a Mediator: Students’ Academic Development Satisfaction and Interpersonal Relationships

Materials and Methods

Participants and Procedure

Questionnaire

University Students’ Academic Development Satisfaction Scale

University Students’ Interpersonal Relationship Scale

University Students’ Social Support Questionnaire

Data Analysis

Reliability and Validity Tests

Results

Descriptive Statistics and Correlation Analysis

Results of SEM Analysis

Test of the Mediating Effects

Discussion and conclusion

Relationship Between Interpersonal Relationships and University Students’ Satisfaction With Their Academic Development

The Mediating Effect of Social Support on Interpersonal Relationships and University Students’ Satisfaction With Their Academic Development

Contributions and Implications

Limitations and Future Directions

Footnotes

Acknowledgements

ORCID iDs

Ethical Considerations

Consent to Participate

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

References