A study on the design of a quantitative model for building the academic culture of financial aid recipients in a new quality productivity environment
Data publikacji: 05 lut 2025
Otrzymano: 21 wrz 2024
Przyjęty: 27 gru 2024
DOI: https://doi.org/10.2478/amns-2025-0080
Słowa kluczowe
© 2025 Zhuojun Wu, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Financial aid is the inevitable trend of student financial aid work in colleges and universities in the new era, and it is also an indispensable part of the parenting system [1]. Financial support for people will be closely integrated with the work of education, improve the important value of financial aid work, and promote the comprehensive development of moral education, not only in the economic aspects of helping students in difficulty but also to further create good conditions for the education of students with financial difficulties, and promote their better growth and success [2-4].
Accurate assistance is the fundamental premise of the implementation of “accurate financial aid”. The establishment of a perfect financial aid object accurate identification index system that is the quantitative assessment of the academic style, the construction of a scientific accurate identification model, and the creation of standardized evaluation criteria so as to accurately identify the financial aid object can improve the effectiveness of the school financial aid work, that is, to avoid the phenomenon of students tampering with the data, poverty and other phenomena such as forgery, but also protect the privacy of the recipients of the financial aid of colleges and universities of great importance to the work of the [5-7].
For colleges and universities to carry out a good academic style construction, the first need to provide a standard comprehensive quantitative assessment of the rules, the student office within the school, the party branch and other important management organizations and personnel to collaborate in the development of specific assessment rules [8-9]. Combined with the specific realities of the university, covering the entire university of all departments of the students enrolled in the time span, the class time, class time, exam time, etc. Specific planning, such as tardiness, early departure and other behaviors for deduction of points, in the results of the assessment of the implementation of the “performance in the class and the final grades” both set the proportion of the sum of the final grades, evaluation of final grades, etc. In the evaluation of scholarships, to carry out the points by the scholarship assessment, the score by the students. In the evaluation of scholarships, the scores are ranked from high to low, and the number of places and the amount of prize money are set [10-12].
“Comprehensive quantitative assessment” refers to the standardized assessment through certain guidelines and indicators to produce a positive effect on something. For example, the quantification of the students’ usual grades and final exam results so that in the management of students’ academic style, colleges and universities can be based on more scientific assessment results and more effective and targeted guidance for students [13-14]. In so many colleges and universities, the comprehensive quantitative assessment method is an effective way to measure the students’ learning atmosphere and performance, which will make them, in the process of learning competition with their surrounding classmates, actively strive for extra points to avoid deductions, to create a positive and optimistic learning atmosphere, the construction of higher education school’s academic style will be further good development [15-16].
This paper firstly discusses the connotation and development path of new quality productivity, highlighting the importance of talent development by governments and educational institutions through subsidised work. Afterwards, it analyzes the problem of academic style construction among the funded recipients to demonstrate the necessity of academic style construction. Multiple linear regression methods were used to construct a quantitative model between the factors affecting academic style construction. Factor analysis was carried out on the questionnaire data. A number of factors were extracted and named, on the basis of which hypotheses were formulated, and multiple linear regression analyses were conducted. After constructing a linear regression model of academic style building, path analysis was conducted to elucidate the relationship between the variables. In order to test the mediating effect between financial aid and school ethos building, the hypothesis of the mediating effect was tested using AMOS 20.0, and the Bootstrap sampling method was used to test whether the mediating effect existed. Finally, the relative effect values of each of the mediating effects were calculated separately, and the differences in indirect effects were compared.
New quality productivity has a rich connotation and deep meaning in the economic category, represents a productivity leap, is science and technological innovation which play a leading role in the productive forces, especially key disruptive technology to achieve breakthroughs in the productive forces, with high efficiency, reflecting the high quality, different from relying on a large number of resource inputs, high consumption of resources and energy, productivity development mode, is free from the traditional growth path, in line with the requirements of high-quality development, is more integrated in the digital era, more reflecting the new connotation of productive forces. It is the productivity that is free from the traditional growth path and meets the requirements of high-quality development, and it is the productivity that is more integrated and embodies new connotations in the digital era [17].
The so-called “new” refers to the new quality of productivity that is different from the general sense of traditional productivity. It is important to recognize the key disruptive technological breakthroughs and their impact on productivity, including new technology, new economies, and new businesses as the main drivers of productivity. The so-called “quality” emphasizes that, on the basis of adhering to the essence of innovation-driven, the breakthroughs in key and disruptive technologies will provide a stronger innovation-driving force for the development of productivity.
In addition to handling the relationship between the government and the market, accelerating the realization of high-level scientific and technological self-reliance and building a modernized industrial system, the important focus point of accelerating the formation of new qualitative productivity is to improve and perfect the science and technology innovation system. Specifically, it is necessary to organically combine government management and market operation, scientifically coordinate, concentrate efforts, optimize mechanisms, and collaborate on research. It is also necessary to optimize the government organization and management functions, promote the organization and management mechanism, overall planning mechanism, project operation mechanism and collective research mechanism innovation, and enhance the government’s ability to serve science and technology innovation and level. Finally, it is necessary to stimulate the vitality of the effective market, ensure the status of enterprises as the main body of innovation, increase the investment of enterprises in R&D, encourage and support enterprises to establish R&D institutions, and actively participate in and lead the major national scientific and technological projects.
In terms of optimizing the system of scientific and technological talents, firstly, we should improve the system of cultivating scientific and technological talents, provide systematic theoretical training and practical training for young scientific and technological innovation talents, grow the team of high-level talents in key national scientific and technological innovation fields, and cultivate innovative scientific and technological talents of a grand scale, reasonable structure and excellent quality. Secondly, we should establish incentive and honor mechanisms for scientific and technological innovation, focusing on increasing incentives for scientific and technological talents who undertake forward-looking, strategic, basic and other key research and development tasks and tilting the salary distribution to scientific and technological talents, as well as increasing publicity for outstanding scientific and technological talents and major scientific and technological research achievements, so as to enable scientific and technological talents to reap the benefits of dual incentives of both the material and the spiritual.
Subsidizing and incentivizing high-calibre personnel is one of the necessary measures to develop new productive capacities. Specifically in the field of higher education, schools must provide subsidies to students in need so as to provide human resources for the development of new productivity. However, the following problems exist in the construction of the academic style of the subsidized students.
Lack of clear learning goals After attending college, students discover a significant gap between their previous life and reality. Some high school teachers instill in students the concept of “it will be easy after going to college”, but they find that it is not so easy after going to college, and it is easy to create a gap in their thinking. Due to the lack of initiative, learning attitude is not serious and lack of self-control, it is easy to lose the learning goal. Some students think that as long as they have a diploma and degree, they can find a job and lack motivation to study. Inadequate knowledge of majors Many students do not know much about their majors before going to school, and after going to school, they find out that it is not what they want. Some students go to school just for the degree and diploma, and don’t care whether the specific specialty they study is of interest or not. Students fundamentally do not have a passion for the profession, which results in a decline in academic performance. Lack of proper learning attitude Some students are late for class, absenteeism, cheating on exams and other violations of school discipline. The purpose of state financial aid is to assist students with financial difficulties in successfully completing their studies. The state’s financial aid is intended to help students study in school. Some students do not have a proper attitude towards learning, do not study hard after receiving a scholarship, and repeat education is ineffective. Participation in the activities of the enthusiasm is not high All kinds of cultural and artistic talent competition activities are a good opportunity to show their talents, play their strengths, and exercise their abilities, which is a specific embodiment of the comprehensive quality of college students. Many students are not interested in the various activities organized by the school, do not pay attention to them, and do not participate in collective activities. There are three main reasons for this: firstly, the student’s concept of class community is relatively poor. Secondly, they are not confident enough in the psychological aspect, and thirdly, they are addicted to social media and ignore the reality of socializing.
It can be seen that in the process of unfolding the financial aid work, the construction of the academic style of the recipients is one of the reasonable expectations of financial aid. This requires us to explore in depth the influencing factors of academic style building, the relationship between financial aid work and academic style building and other issues in order to carry out financial aid work more effectively. The multiple linear regression method is a simple and effective method for quantitative modeling of multiple independent variables. For this reason, this paper utilizes the concept of multiple linear regression to establish a quantitative model of the factors that affect the construction of academic styles. The mathematical model of multiple linear regression is briefly reviewed below.
A multiple linear regression model is a linear regression model that contains multiple independent variables. It is used to estimate the corresponding changes in the dependent variable through the changes in the independent variables. Generally, the independent and dependent variables in this model are quantitative values, and if the independent and dependent variables are not quantitative, they need to be changed to quantitative values through certain transformations before regression analyses can be performed. The reason why the multiple linear regression model is used is because the factors that affect things cannot be single, but must be affected by several factors together. The optimal combination of multiple independent variables to explain the dependent variable gives better results than a single independent variable to explain the dependent variable [18]. The general form of the multiple linear regression model is formulated as:
In equation (1),
If there is
Its matrix form is shown in equation (4):
The simplified form is shown in equation (5):
The questionnaire was designed to cover all the factors that affect the academic culture of undergraduates. The questionnaire consists of 25 questions, which are asked in affirmative form, and the Likert five scale method is adopted and the options are designed as degree scales, which are not at all, not at all, not at all, basically, relatively and completely. The questionnaire was tested for reliability and validity, and the question items with poor reliability and validity were deleted or redesigned, and the reliability coefficient ɑ = 0.943 and KMO = 0.952, indicating that the questionnaire meets the requirements of reliability and validity and is suitable for factor analysis. Taking all undergraduate students of University G as the research object, 723 questionnaires were distributed, and 687 valid questionnaires were recovered, with an effective recovery rate of 95.02%, which is a good recovery situation.
Table 1 shows the factor analysis test. The table is analyzed to extract factors and determine the amount of information extracted from them. From the table, it can be seen that the factor analysis extracted a total of five factors, and the cumulative variance explained after rotation is 80.086%, which indicates that the questionnaire has a high degree of validity.
Factor analysis test
| NO. | Eigenvalue | Explained variance ratio before rotation | Explained variance ratio after rotation | ||||||
|---|---|---|---|---|---|---|---|---|---|
| EV | CVE% | C.P.% | EV | CVE% | C.P.% | EV | CVE% | C.P.% | |
| 1 | 17.747 | 58.838 | 58.838 | 17.747 | 58.838 | 58.838 | 6.264 | 21.806 | 21.806 |
| 2 | 2.731 | 9.231 | 68.069 | 2.731 | 9.231 | 68.069 | 5.632 | 18.846 | 40.6525 |
| 3 | 1.502 | 5.232 | 73.301 | 1.502 | 5.232 | 73.301 | 5.223 | 17.585 | 58.2379 |
| 4 | 1.236 | 3.947 | 77.249 | 1.236 | 3.947 | 77.249 | 3.935 | 12.657 | 70.8954 |
| 5 | 0.864 | 2.838 | 80.086 | 0.864 | 2.838 | 80.086 | 2.717 | 9.474 | 80.3694 |
| 6 | 0.851 | 2.222 | 82.309 | ||||||
| 7 | 0.691 | 1.998 | 84.307 | ||||||
| 8 | 0.508 | 1.354 | 85.661 | ||||||
| 9 | 0.492 | 1.270 | 86.931 | ||||||
| 10 | 0.251 | 1.230 | 88.161 | ||||||
Table 2 shows the table of factor loading coefficients after rotation. The data of this study were rotated using the maximum variance rotation method to find out the correspondence between factors and research items. The table shows the information extracted by the factors for the research items and the correlation between the factors and the research items. From the above table, it can be seen that all the research items correspond to a common degree value higher than 0.4, which means that there is a strong correlation between the research items and the factors, and the factors can extract the information effectively. After ensuring that the factors can extract most of the information of the research items, the correspondence between the factors and the research items is then analysed, and the absolute value of the factor loading coefficients of each factor is higher than 0.4, which means that there is a correspondence between the research items and the factors. The results of the factor analysis show that the questions designed in the questionnaire have a strong correlation with the factors influencing the construction of academic ethics to be studied, and the five dimensions and questions designed in the questionnaire have high validity.
Table of Load Factor after Rotation
| Name | Factor load factor | Communality | ||||
|---|---|---|---|---|---|---|
| Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 | ||
| Q1 | 0.289 | 0.388 | 0.701 | 0.184 | 0.105 | 0.825 |
| Q2 | 0.332 | 0.400 | 0.765 | 0.143 | 0.017 | 0.804 |
| Q3 | 0.248 | 0.287 | 0.795 | 0.199 | 0.126 | 0.774 |
| Q4 | 0.410 | 0.247 | 0.690 | 0.178 | 0.077 | 0.695 |
| Q5 | 0.210 | 0.388 | 0.677 | 0.239 | 0.174 | 0.738 |
| Q6 | 0.183 | 0.394 | 0.756 | 0.174 | 0.199 | 0.785 |
| Q7 | 0.166 | 0.354 | 0.692 | 0.283 | 0.229 | 0.748 |
| Q8 | 0.224 | 0.863 | 0.374 | 0.161 | 0.119 | 0.928 |
| Q9 | 0.252 | 0.783 | 0.331 | 0.190 | 0.118 | 0.882 |
| Q10 | 0.173 | 0.809 | 0.328 | 0.201 | 0.165 | 0.877 |
| Q11 | 0.211 | 0.742 | 0.357 | 0.175 | 0.199 | 0.852 |
| Q12 | 0.151 | 0.800 | 0.267 | 0.162 | 0.208 | 0.835 |
| Q13 | 0.209 | 0.822 | 0.288 | 0.144 | 0.183 | 0.825 |
| Q14 | 0.637 | 0.278 | 0.221 | 0.286 | 0.395 | 0.817 |
| Q15 | 0.642 | 0.212 | 0.276 | 0.327 | 0.265 | 0.718 |
| Q16 | 0.688 | 0.218 | 0.271 | 0.327 | 0.355 | 0.789 |
| Q17 | 0.661 | 0.303 | 0.267 | 0.218 | 0.216 | 0.734 |
| Q18 | 0.373 | 0.317 | 0.193 | 0.126 | 0.742 | 0.880 |
| Q19 | 0.412 | 0.277 | 0.178 | 0.100 | 0.707 | 0.822 |
| Q20 | 0.576 | 0.315 | 0.194 | 0.193 | 0.585 | 0.853 |
| Q21 | 0.785 | 0.178 | 0.253 | 0.262 | 0.253 | 0.851 |
| Q22 | 0.809 | 0.245 | 0.217 | 0.273 | 0.245 | 0.830 |
| Q23 | 0.813 | 0.182 | 0.241 | 0.345 | 0.189 | 0.828 |
| Q24 | 0.822 | 0.173 | 0.218 | 0.263 | 0.109 | 0.843 |
| Q25 | 0.459 | 0.170 | 0.202 | 0.731 | -0.093 | 0.763 |
The five extracted factors were named as five: school funding, teacher quality, individual students, family factors and social factors, respectively, and the five variables were analysed by multiple regression.
Based on the results of the above analyses, the following hypotheses are proposed:
H1: Individual students’ factors will have some influence on building school ethos.
H2: Students’ family factors will have some influence on the construction of school learning style.
H3: Teachers’ quality will have some influence on the construction of school ethos.
H4: School financial aid has a certain influence on the construction of school ethos.
H5: Social factors will affect the construction of school ethos.
According to the above research hypotheses, multiple linear regression analyses and path analyses are carried out to explore the relationship between the effects of several independent variables on the dependent variables and their paths.
Table 3 shows the results of multiple linear regression analyses, in which 25 variables are indicated by lower case letters. The regression analyses indicate that the VIF values among multiple independent variables vary from 1.383 to 2.638, and there is no significant covariance issue. The multiple regression model in this study had an R-square of 0.48, suggesting that several independent variables had a moderate amount of explanation for the dependent variable. The results of the regression analyses showed that among the several independent variables included, individual persistence in learning (c) (p=0.000, t=4.659), individual study habits (d) (p=0.037, t=2.121), teachers’ level of instruction (e) (p=0.001, t=3.414), school financial assistance in place (n) (p=0.001, t=3.239 ), parent-child relationship (s) (p=0.018, t=2.523,), role model figure (v) (p=0.036, t=1.971), and internet influence (x) (p=0.041, t=1.901) had a significant effect on school spirit.
Multivariate linear regression analysis results
| DV | IV | Unnormalized coefficient | Normalized coefficient | t | Sig. | Common linear statistics | R2 | |
|---|---|---|---|---|---|---|---|---|
| B | SE | Beta | VIF | |||||
| Academic ethos | Cons. | 0.691 | 0.150 | 3.649 | 0.000 | 0.480 | ||
| S | 0.002 | 0.026 | 0.013 | 0.574 | 0.544 | 1.936 | ||
| b | 0.051 | 0.046 | 0.065 | 1.638 | 0.081 | 2.262 | ||
| 0.055 | 0.200 | 4.659** | 0.000 | 2.197 | ||||
| 0.041 | 0.103 | 2.121* | 0.037 | 2.084 | ||||
| 0.025 | 0.159 | 3.414** | 0.001 | 2.421 | ||||
| f | 0.032 | 0.060 | 0.037 | 0.661 | 0.526 | 2.386 | ||
| g | 0.038 | 0.032 | 0.052 | 1.215 | 0.226 | 2.011 | ||
| h | 0.017 | 0.029 | -0.023 | -0.298 | 0.791 | 2.485 | ||
| i | 0.018 | 0.030 | -0.037 | -0.833 | 0.416 | 1.684 | ||
| j | 0.064 | 0.032 | 0.058 | 1.288 | 0.184 | 2.520 | ||
| k | 0.037 | 0.048 | 0.021 | 0.453 | 0.647 | 2.436 | ||
| l | 0.014 | 0.048 | 0.010 | 0.523 | 0.592 | 2.139 | ||
| m | 0.056 | 0.033 | 0.047 | 1.055 | 0.277 | 2.521 | ||
| 0.032 | 0.149 | 3.239** | 0.000 | 2.042 | ||||
| o | 0.064 | 0.055 | -0.072 | -1.466 | 0.150 | 2.532 | ||
| p | 0.017 | 0.030 | 0.029 | 0.335 | 0.734 | 2.310 | ||
| q | -0.004 | 0.060 | 0.026 | 0.126 | 0.931 | 2.638 | ||
| r | 0.045 | 0.047 | -0.057 | -1.046 | 0.291 | 2.276 | ||
| 0.034 | 0.103 | 2.523* | 0.018 | 1.896 | ||||
| t | 0.015 | 0.048 | 0.055 | 1.297 | 0.208 | 1.999 | ||
| u | 0.036 | 0.033 | -0.045 | -1.029 | 0.303 | 2.216 | ||
| 0.040 | -0.093 | 1.971* | 0.036 | 2.515 | ||||
| w | 0.058 | 0.049 | -0.024 | -0.413 | 0.683 | 1.383 | ||
| 0.028 | 0.084 | 1.901* | 0.041 | 1.428 | ||||
Comparing the standardised Beta values, in descending order of influence on academic style building, the influencing factors were individual study persistence (c) (β=0.2), teacher’s level of instruction (e) (β=0.159), school financial aid (n) (β=0.149), parent-child relationship (s) (β=0.103), individual study habits (d) (β=0.103 ), internet influence (x) (β=0.084), and role model figure influence (v) (β=-0.093).
The linear regression model constructed in this paper for the building of school spirit is as follows:
It can be seen that financial aid has a contribution amount of 0.089 units in building academic climate. It can also be proved that hypotheses H1, H2, H3, H4, and H5 are valid and that individual students and their families, teachers, financial aid, and society all have an impact on academic climate building.
In order to explore the relationship between several independent variables on the dependent variables, the following research hypotheses are proposed:
H6: There is a mediating role of teacher factors between school financial aid and individual student factors.
H7: There is a mediating role of teacher factors between school financial aid and school ethos.
H8: There is a mediating role of individual student factors between school financial aid and school ethos.
H9: There is a mediating role of individual students’ factors between teachers’ factors and school ethos.
The research model was constructed according to the corresponding research hypotheses to explore the paths taken by several independent variables on the dependent variable. The results of the path analysis are shown in Table 4. According to the results of the path analysis in Table 4, it was found that the influence relationships among the four factors constructed by the hypothetical model of this study were all verified.
Path analysis
| DV | IV | Unnormalized coefficient | Normalized coefficient | t | Sig. | R^2 | |
|---|---|---|---|---|---|---|---|
| B | SE | Beta | |||||
| Teacher | Cons. | 2.270 | 0.128 | 16.710 | 0.000 | 0.303 | |
| Imburse | 0.507 | 0.027 | 0.551 | 16.760 | 0.000 | ||
| Individual | Cons. | 0.347 | 0.177 | 1.923 | 0.048 | 0.392 | |
| Imburse | 0.250 | 0.071 | 0.188 | 5.961 | 0.000 | ||
| Teacher | 0.578 | 0.034 | 0.469 | 12.777 | 0.000 | ||
| Academic ethos | Cons. | 1.013 | 0.177 | 5.539 | 0.000 | 0.331 | |
| Individual | 0.414 | 0.040 | 0.432 | 10.753 | 0.000 | ||
| Teacher | 0.247 | 0.060 | 0.222 | 5.077 | 0.000 | ||
It is known that all hypothesised paths are significant (p<0.001). Specifically, for every 1 unit increase in funding, the individual factor increases by 0.250 units. For every 1 unit increase in teacher factors, the individual factor increases by 0.578 units. The teacher factor increases by 0.507 units for each 1-unit increase in funding efforts. For every 1 unit increase in the individual factor, the school climate will increase by 0.414 units. For every 1 unit increase in the teacher factor, the school climate increases by 0.247 units.
This study used AMOS 20.0 to analyze to test the above hypothesis of mediating effect. In this study, the Bootstrap sampling method was mainly applied to test the existence of a mediating effect. If the confidence interval does not contain 0, it means that there is a mediating effect, and vice versa, it means that there is no mediating effect [19]. The test results of the hypothesized relationship of the mediating effect between school ethos and its influencing factors are shown in Table 5, which presents the test results of the hypothesis of the mediating effect of repeated sampling 6,000 times with the Bootstrap method.
The test result of the mediating effect
| Path | Point estimation | Product of coef. | Bootstrapping | ||||
|---|---|---|---|---|---|---|---|
| Bias-Corrected 95%CI | Percentile 95%CI | ||||||
| SE | Z | Lower | Upper | Lower | Upper | ||
| Indirect effect | |||||||
| Imb.→teac.→stu. | 0.321 | 0.056 | 8.118 | 0.238 | 0.391 | 0.236 | 0.361 |
| Imb.→teac.→stu.→AE | 0.135 | 0.046 | 6.652 | 0.099 | 0.196 | 0.094 | 0.164 |
| Imb.→stu.→AE | 0.097 | 0.022 | 3.704 | 0.069 | 0.194 | 0.059 | 0.191 |
| Teac.→stu.→AE | 0.272 | 0.019 | 7.350 | 0.190 | 0.328 | 0.208 | 0.320 |
| Imb.→teac.→AE | 0.095 | 0.042 | 4.180 | 0.087 | 0.182 | 0.085 | 0.217 |
| Total effect | |||||||
| Imb.→stu. | 0.549 | 0.037 | 12.047 | 0.461 | 0.647 | 0.435 | 0.689 |
| Imb.→AE | 0.380 | 0.048 | 10.576 | 0.277 | 0.391 | 0.308 | 0.425 |
| Teac.→AE | 0.491 | 0.010 | 10.216 | 0.378 | 0.574 | 0.461 | 0.581 |
| Direct effect | |||||||
| Imb.→stu. | 0.211 | 0.060 | 4.816 | 0.168 | 0.370 | 0.146 | 0.373 |
| Imb.→AE | 0.202 | 0.020 | 4.044 | 0.140 | 0.305 | 0.127 | 0.319 |
| Teac.→AE | 0.265 | 0.060 | 4.568 | 0.135 | 0.335 | 0.150 | 0.360 |
According to the test criteria of mediation effect, it can be seen that there is a significant mediation effect of teachers’ factors between financial aid work and individual factors (Z=8.118), there is a significant mediation effect of teachers’ factors and individual factors between school financial aid and school learning style construction (Z=6.652), and the mediation effect of individual factors in the “financial aid work→individual factors→school learning style construction”. The mediating effect of individual factors in “financial aid → individual factor → school academic style building” was also proved (Z=3.704), and other mediating effects were also verified accordingly.
In order to compare the importance of the three intermediary effects on the relationship path between school funding and school learning style construction, the relative effect values of the three intermediary effects were calculated, and the calculation results are shown in Table 6. Among them, ind1 is “funding work → teacher factors → individual students → construction of school learning style”, ind2 is “funding work → individual students → construction of school learning style”, ind3 is “funding work → teacher factors → construction of school learning style”.
The proportion of the relative effect of the intermediary effect
| Effect value | Bootstrapping | Relative effect | ||||
|---|---|---|---|---|---|---|
| Bias-Corrected 95%CI | Percentile 95%CI | |||||
| Lower | Upper | Lower | Upper | |||
| Total effect | 0.323 | 0.310 | 0.490 | 0.300 | 0.476 | |
| Ind1 | 0.092 | 0.097 | 0.184 | 0.093 | 0.171 | 37.89% |
| Ind2 | 0.102 | 0.054 | 0.149 | 0.031 | 0.147 | 30.17% |
| Ind3 | 0.122 | 0.064 | 0.183 | 0.062 | 0.165 | 35.67% |
It can be seen that the relative effect value of the first mediation effect is 37.89%, which accounts for the highest proportion, indicating that its mediation effect is the strongest among the three mediation paths. The third intermediation effect was followed by the relative effect value, which accounts for 35.67%, followed by the intermediation effect. Finally, the second intermediation path, with a relative effect value of 30.17%, is the lowest among the three intermediation effects.
According to the test results of the three mediating effects on the path of the relationship between the financial aid work affecting the construction of school ethos, it is further carried out whether there is a significant difference between the three mediating paths and the results of the comparison of the differences in the indirect effects are shown in Table 7. According to the results of the comparison of the differences in indirect effects presented in Table 7, although the three indirect effects were tested by Z-test, bias correction method, and percentile method, all of them existed, but after the two-by-two comparison of the mediating effects of the three paths did not present a significant difference.
Comparison of indirect effects
| Comparison of indirect effects | Point Estimate | Product of coef. | Bootstrapping | ||||
|---|---|---|---|---|---|---|---|
| Bias-Corrected 95%CI | Percentile 95%CI | ||||||
| SE | Z | Lower | Upper | Lower | Upper | ||
| ind1 | 0.139 | 0.018 | 6.640 | 0.074 | 0.169 | 0.048 | 0.153 |
| ind2 | 0.110 | 0.039 | 3.699 | 0.096 | 0.164 | 0.066 | 0.177 |
| ind3 | 0.139 | 0.017 | 4.219 | 0.056 | 0.200 | 0.068 | 0.202 |
| ind1-ind2 | 0.018 | 0.051 | 0.619 | -0.059 | 0.076 | -0.071 | 0.095 |
| ind1-ind3 | 0.013 | 0.017 | 0.027 | -0.088 | 0.117 | -0.074 | 0.087 |
| ind2-ind3 | -0.029 | 0.046 | -0.418 | -0.129 | 0.062 | -0.116 | 0.055 |
Therefore, Hypothesis 6, Hypothesis 7, Hypothesis 8 and Hypothesis 9 were all tested, indicating that all four mediating effects were significantly present.
The article utilized multiple linear regression to construct a quantitative model of school spirit construction. According to the results of the regression analysis, the factors of individual study persistence, individual study habits, teacher’s teaching level, school financial aid work in place, parent-child relationship, role model figure and network influence all have a significant impact on the school’s academic style construction. Further path analysis results showed that for every 1 unit increase in financial aid efforts, the individual student factor increased by 0.250 units, and the teacher factor increased by 0.507 units. For every 1 unit increase in the individual factor, there is an increase of 0.414 units in academic climate building. For every 1 unit increase in the teacher factor, there is an increase of 0.247 units in school climate. The mediating effect was tested, and it was found that the mediating effect exists between the teacher’s factor in the subsidized work and individual factor, between the teacher’s factor and individual factor in the school subsidy and the construction of school ethos, and between the individual factor in the subsidized work and the construction of school ethos, of which the mediating effect of the path of “subsidized work→teacher’s factor→individual student→construction of school ethos” is 37.89%. The relative effect value is 37.89%, accounting for the highest proportion.
In summary, the construction of school ethos is influenced by financial aid work, which is influenced by the factors of teachers and individual students. Therefore, it is crucial to prioritize the quality of teacher training and the education of students when carrying out financial aid work.