American Journal of Economic and Management Business p-ISSN: XXXX-XXXX e-ISSN: 2835-5199 Vol. 4 No. 6 June 2025 676 The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung Agus Sulistyo Budi 1 , Ronny Samsul Bahri 2 * Sekolah Tinggi Ilmu Ekonomi Harapan Bangsa, Indonesia 1 BINUS University, Indonesia 2 Email: mm-24081@students.ithb.ac.id, ronny.bahri@binus.ac.id Abstract This study aims to analyze the influence of service quality, price, and brand image on the purchase intention of high school XI students in choosing a guidance institution in the city of Bandung. Data were collected through questionnaires distributed to 200 respondents and analyzed using multiple linear regression. The results showed that service quality and price had a positive and significant influence on students' purchase intention, while brand image did not show a significant influence. Simultaneously, these three variables can explain 73.6% of the variation in students' purchase intentions. These findings indicate that although brand image does not play a dominant role, service quality and price are more important factors in influencing students' decisions. This research provides insights for guidance institutions to improve the quality of services and pricing in accordance with the benefits provided to attract students' buying interest. Keywords: service quality; price; brand image INTRODUCTION Education in Indonesia continues to undergo significant changes along with technological developments and increasingly diverse societal needs. One of the sectors that is also growing rapidly in the education industry is tutoring. In recent years, public interest in tutoring institutions, especially those based on technology, has experienced a tremendous surge (Kaniawati, 2021; Patacsil & Tablatin, 2017). People are beginning to realize that quality education does not only come from formal educational institutions, but also through additional guidance that can support the learning process more effectively. The development of tutoring in the city of Bandung has increased rapidly, reflecting the high public demand for additional education outside of formal school (Gisbert & Rivas, 2021; Hrastinski et al., 2018; Kurniawan et al., 2023; Newar et al., 2023; Seraj, 2023). This is due to the dense curriculum and emphasis on test scores as a measure of success, which encourages students and parents to seek additional alternatives such as tutoring. Some of the leading tutoring institutions in Bandung, such as Ganesha Operation (GO), Sony Sugema College (SSC), and Bimbel Tridaya, have been operating for decades and offer quality programs to help students prepare for exams and pursue higher education. In addition, the emergence of hybrid-based tutoring institutions, such as the Grissham Hybrid Course (GHC), provides flexibility for students who want a quality learning experience with a more flexible approach (Kee et al., 2024; Masalimova et al., 2021; Molenaar, 2022; Mulaudzi et al., 2023; Sarwendah et al., 2023). GHC is the first Hybrid Education institution in Indonesia that optimally combines traditional and digital learning methods. With an approach tailored to students' potential, Grissham provides academic programs from elementary to professional levels, with the goal of creating an empowering learning environment. The institution supports student development through quality teaching and modern facilities that focus on flexible and innovative learning experiences (grissham.com). The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 677 The advantages of GHC are that it provides quality superior programs, namely, tutoring programs to enter official schools such as Akpol or guidance and counseling to become Police Officers. This makes it a special attraction for parents of students in choosing tutoring. The choice of parents of students becomes more varied and can actually direct their children from an early age to take guidance programs from elementary school to official school guidance, or can take guidance and counseling options to become Police Officers (grissham.co.id). According to Tjiptono (2019), the service quality factor is important in influencing consumers' decision intentions to determine something. Guidance institutions that are able to offer more than just standard services, for example by providing better facilities, more professional teaching, or more competitive prices, will certainly attract more consumer interest. The difference in the research of Puspitasari and Herman (2018) is that although the quality of service has a positive effect on the intention of choosing a tutoring institution, the results are not significant. According to Yusuf (2022), tutoring institutions need to improve the quality of their services and strengthen their competitiveness to remain relevant in the midst of increasingly fierce competition. In response to such strong competition, each institution seeks to improve the quality of their services to attract more students. One way is by presenting a variety of learning programs offered in various price ranges. According to Kotler and Keller (2016), price is one of the main elements in the marketing mix that can influence consumers' perception of value and purchasing decisions. According to Tjiptono (2019), in addition to price, the service quality factor is also important in influencing consumers' intention to decide something. The opposite is true from the research of Sari and Wibowo (2021), which found that in the education industry, the price factor is not always the main factor because prospective students tend to consider the reputation and results that have been achieved by the alumni of the institution. Tutoring institutions are constantly striving to improve the quality of service and establish the right pricing strategy to maintain their brand image. Keller (2003) states that brand image is one of the valuable assets for a company, which requires time and consistent effort to build. A strong brand image can strengthen a company's reputation by bringing its good name, as well as being a factor that affirms the quality and credibility of the company. Conversely, the company's image also has an influence on consumers' perception of the brands offered, which can ultimately influence selection decisions. The results of the research by Puspitasari & Hermawan (2018) also state the same thing: that brand image variables have a positive and significant influence on selection decisions. This shows that the better the brand image the institution has, the higher the likelihood that students will choose to join. This research focuses on three main variables, namely service quality, price, and brand image, which are often the main considerations for students and parents in determining tutoring choices. Service quality plays a role in providing an effective and comfortable learning experience for students, while price is a factor that affects the affordability and perception of the value of a service. On the other hand, brand image helps build the trust and attractiveness of tutoring institutions in the eyes of prospective students. This study aims to analyze the influence of service quality, price, and brand image on the purchase intention of grade XI students in choosing tutoring in the city of Bandung. The formulation of the problem raised in this study is whether service quality, price, and brand image affect purchase intention, both individually and together. This study aims to identify and analyze the influence of each of these factors on students' decisions in choosing tutoring services. The benefit of this research is that it provides practical insights for tutoring institutions in designing more effective marketing strategies by improving service quality, The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 678 pricing, and brand image. In addition, this study also provides theoretical benefits by strengthening empirical evidence regarding factors that affect purchase intention in the non- formal education sector, as well as enriching the literature related to marketing strategies that can be applied by educational institutions to increase competitiveness in the education industry. METHOD The research was conducted in the city of Bandung, which was chosen because it had many guidance institutions with a variety of different services, prices, and brand images. The research period lasted from February to June 2025. This study used a quantitative method with a causal associative approach, analyzing the causal relationship between variables. The population of this study consisted of high school XI students in the city of Bandung, with a sample of 200 respondents taken using the purposive sampling method. The research variables included service quality (X1), price (X2), and brand image (X3) as independent variables, as well as purchase intention (Y) as the dependent variable. Data were collected through questionnaires distributed to respondents and analyzed using statistical techniques, including validity and reliability tests, exploratory factor analysis (EFA), and multiple linear regression to test the influence between variables. RESULTS AND DISCUSSION Research Analysis Results This study aims to determine the influence of independent variables, namely Service Quality, Price, and Brand Image on dependent variables, namely Purchase Intention in choosing tutoring services for grade XI students. To test the validity, reliability, and feasibility of the instrument and analysis model, a series of statistical tests were carried out which are described as follows: Validity Test The validity test was carried out to find out the extent to which the research instrument was able to measure what should be measured. An instrument is said to be valid if the correlation value between items to the total score is greater than the r-table. All items in the Quality, Price, Branding, and Purchase Intent variables show significant correlation values, so all items are valid. The validity test in this study was carried out on 203 respondents by filling out a questionnaire of 21 questions, the validity test using a significant level (α) of 5% or 0.05. To obtain the r-value of the table, first look for Df = N-2 = 203 – 2 = 201 so that the r-value of the table = 0.138. You can see the table below which is the result of data processing through SPSS: Table 1. Valditas Test Results Independent Variables (Quality of Service) Indicator r-Calculate value R-table values r-Calculate > r-Table Sig Sig < 0.05 Information X1.1 0.654 0.138 0.000 0.05 VALID X1.2 0.863 0.000 X1.3 0.908 0.000 X1.4 0.919 0.000 X1.5 0.945 0.000 Price Independent Variables Indicator r-Calculate value R-table values r-Calculate > r-Table Itself Sig < 0.05 Information X2.1 0.960 0.138 0.000 0.05 VALID X2.2 0.946 0.000 The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 679 Independent Variables (Quality of Service) X2.3 0.945 0.000 X2.4 0.931 0.000 X2.5 0.949 0.000 X2.6 0.940 0.000 Independent Variables of Brand Image Indicator r-Calculate value R-table values r-Calculate > r-Table Itself Sig < 0.05 Information X3.1 0.953 0.138 0.000 0.05 VALID X3.2 0.985 0.000 X3.3 0.979 0.000 X3.4 0.346 0.000 X3.5 0.957 0.000 Variable Dependent Purchase Intent Indicator r-Calculate value R-table values r-Calculate > r-Table Itself Sig < 0.05 Information Y1.1 0.97 0.138 0.000 0.05 VALID Y1.2 0.961 0.000 Y1.3 0.977 0.000 Y1.4 0.951 0.000 Y1.5 0.960 0.000 Source : Data Processed Based on the results of the validity test, all statement items on each variable in this study showed a significant correlation value to the total score of the variable. This indicates that each indicator used in measuring the variables of Service Quality, Price, Brand Image, and Purchase Intent is valid and can be used in the further analysis process. Exploratory Factor Analysis (EFA) Results Variable Reliability Reliability tests are carried out to measure the extent to which the research instrument is able to provide consistent results when repeated measurements are made on the same object. High reliability indicates that each item in the questionnaire is capable of providing stable and reliable results in representing the constructed being measured. In this study, the reliability test was carried out using the Cronbach's Alpha through the help of SPSS software version 26. According to Ghozali (2018), the value of Cronbach’s Alpha those below 0.60 are considered unreliable, values between 0.60 to 0.70 are acceptable, and values above 0.80 indicate good reliability. The following are the results of data processing that has been carried out: Table 2. Reliability Tests Variabel Cronbach's Alpha Batasan Information Quality of Service 0.903 > 0.60 Reliabel Excellent Price 0.976 Brand Image 0.926 Purchase Intent 0.981 Source : Data Processed Based on the fact that all variables in this study have a very high Cronbach's Alpha value. The Service Quality variable obtained a reliability value of 0.903, Price of 0.976, Brand Image of 0.926, and Purchase Intention of 0.981. All of these values are above the minimum threshold of 0.60, even exceeding the value of 0.90 which indicates an excellent level of reliability. Thus, all instruments used in measuring the four variables can be declared reliable, because they have shown high internal consistency. This indicates that the items in The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 680 the questionnaire are able to measure constructs consistently and are reliable for use in advanced analysis. Results of KMO Data Processing, Barlett's Test, and MSA Variables After all indicators in each variable are declared reliable, the next step is to test the feasibility of the sample and the relationship between indicators. The purpose of this stage is to ensure that the data used is eligible to be carried out Exploratory Factor Analysis (EFA). This test is important to assess the extent to which the indicators in a variable have a strong relationship or correlation with each other. If the indicators show a significant relationship, then it shows that they are indeed worthy of being grouped in the same construct or dimension. The high correlation between indicators is an indication that the structure of the factors formed will be stable and representative. Thus, this analysis is the basis for ensuring the existence of Meaningful relationships (non-zero correlation) between indicators before proceeding to the stage of forming factor structures through the exploration process. The following is attached the results of data processing from each variable: Quality of Service Table 3. Scores of SMEs & Bartlett's Test KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .824 Bartlett's Test of Sphericity Approx. Chi-Square 859.652 Df 10 Itself. .000 Source : Data Processed Based on the results of data processing, the value of the SME for the Service Quality variable is 0.824, which indicates that the data has excellent sample sufficiency. The closer it is to number 1, the more suitable the data is to be analyzed using factor analysis. In addition, the significance value of Bartlett's Test is 0.000 (< 0.05) shows that there is a significant correlation between the indicators in this variable. This means the sphericity assumptions are met, and the data is worth exploring further. Table 4. KMO-MSA Values Anti-image Matrices X1.1 X1.2 X1.3 X1.4 X1.5 Anti-image Covariance X1.1 .530 -.151 -.098 .060 -.025 X1.2 -.151 .303 -.066 .008 -.074 X1.3 -.098 -.066 .281 -.080 -.020 X1.4 .060 .008 -.080 .179 -.116 X1.5 -.025 -.074 -.020 -.116 .156 Anti-image Correlation X1.1 .828a -.378 -.255 .195 -.086 X1.2 -.378 .867a -.227 .033 -.341 X1.3 -.255 -.227 .894a -.355 -.095 X1.4 .195 .033 -.355 .761a -.695 X1.5 -.086 -.341 -.095 -.695 .787a a. Measures of Sampling Adequacy(MSA) Source : Data Processed The results of the MSA test on each indicator showed values above 0.70, such as X1.1 (0.828), X1.2 (0.867), X1.3 (0.894), X1.4 (0.761), and X1.5 (0.787). These values reinforce the finding that each indicator has good feasibility to be included in the factor analysis. The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 681 Table 5. Nilai Total Variance Explained Source : Data Processed In the results of Total Variance Explained, the first factor had an eigenvalue of 3,761, and was able to explain 75,216% of the initial total variance. After the extraction process, this factor was still able to explain 69.864%, which means that all indicators in this variable are concentrated to form one main factor. This shows that the Quality of Service dimension is one-dimensional and strong enough to form a complete construct. Table 6. Comunalities and matrix values Source : Data Processed From the Communalities table, it can be seen that the extraction value of all indicators is above 0.50, which means that the contribution of each indicator to factor formation is very good. The X1.5 indicator showed the highest value of 0.860, followed by X1.3 (0.783) and X1.2 (0.732), although there is one indicator below 0.50 but is still considered to be able to contribute even though it is not as high as other indicators. Meanwhile, the results of the Factor Matrix also show that all indicators have a strong loading factor, where X1.5 reaches 0.927, X1.3 reaches 0.885, and X1.2 reaches 0.855. This reinforces that each item has a high contribution force to the constructed construct. Total Variance Explained Factor Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 3.761 75.216 75.216 3.493 69.864 69.864 2 .668 13.361 88.577 3 .256 5.113 93.690 4 .221 4.412 98.102 5 .095 1.898 100.000 Extraction Method: Principal Axis Factoring. Factor Matrixa Factor 1 X1.1 .616 X1.2 .855 X1.3 .885 X1.4 .860 X1.5 .927 Extraction Method: Principal Axis Factoring. Communalities Initial Extraction X1.1 .470 .379 X1.2 .697 .732 X1.3 .719 .783 X1.4 .821 .739 X1.5 .844 .860 Extraction Method: Principal Axis Factoring. The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 682 Price Table 7. Grades KMO & Bartlett’s Test KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .948 Bartlett's Test of Sphericity Approx. Chi-Square 1736.069 Df 15 Itself. .000 Source : Data Processed Based on the results of the data processing, the KMO value for the Price variable is 0.948, which indicates that the data has excellent sample sufficiency. The closer it is to number 1, the more suitable the data is to be analyzed using factor analysis. In addition, the significance value of Bartlett’s Test by 0.000 (< 0.05) shows that there is a significant correlation between the indicators in this variable. This means the assumption sphericity fulfilled, and the data is worth exploring further. Table 8. KMO-MSA Values Anti-image Matrices X2.1 X2.2 X2.3 X2.4 X2.5 X2.6 Anti-image Covariance X2.1 .111 -.044 -.024 -.037 -.030 -.019 X2.2 -.044 .143 -.026 -.018 -.029 -.016 X2.3 -.024 -.026 .151 -.023 -.034 -.035 X2.4 -.037 -.018 -.023 .187 -.014 -.039 X2.5 -.030 -.029 -.034 -.014 .139 -.034 X2.6 -.019 -.016 -.035 -.039 -.034 .166 Anti-image Correlation X2.1 .929a -.350 -.183 -.258 -.242 -.143 X2.2 -.350 .946a -.178 -.109 -.205 -.102 X2.3 -.183 -.178 .953a -.137 -.233 -.220 X2.4 -.258 -.109 -.137 .960a -.087 -.219 X2.5 -.242 -.205 -.233 -.087 .948a -.223 X2.6 -.143 -.102 -.220 -.219 -.223 .955a a. Measures of Sampling Adequacy(MSA) Source : Data Processed The MSA test results on each indicator showed values above 0.90, such as X2.1 (0.929), X2.2 (0.946), X2.3 (0.953), X2.4 (0.960), and X2.5 (0.948), and X2.6 (0.955). These values reinforce the finding that each indicator has excellent feasibility for inclusion in the factor analysis. Tabel 9. Nilai Total Variance Explained Source : Data Processed Total Variance Explained Fact or Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 5.360 89.333 89.333 5.232 87.201 87.201 2 .166 2.772 92.105 3 .148 2.463 94.569 4 .124 2.068 96.637 5 .114 1.893 98.530 6 .088 1.470 100.000 Extraction Method: Principal Axis Factoring. The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 683 In the results of Total Variance Explained, the first factor had an eigenvalue of 5,360, and was able to explain 89.33% of the initial total variance. After the extraction process, this factor is still able to explain 87.201%, which means that all indicators in this variable are concentrated in forming one main factor. This shows that the Price dimension is one- dimensional and very strong in forming a whole construct. Table 10. Comunalities and matrix values Source : Data Processed From the Communalities table, it can be seen that the extraction value of all indicators is above 0.50, which means that the contribution of each indicator to factor formation is very good. The X2.1 indicator shows the highest value of 0.914, the average indicator is above 0.8, Meanwhile, the results of the Factor Matrix also show that all indicators have a strong loading factor, where all indicators reach more than 0.9, This reinforces that each item has a high contribution strength to the constructed construct. Brand Image Table 11. Value of the SME & Bartlett's Test Source : Data Processed Based on the results of the data processing, the value of the SME for the Brand Image variable is 0.855, which indicates that the data has excellent sample sufficiency. The closer it is to number 1, the more suitable the data is to be analyzed using factor analysis. In addition, the significance value of Bartlett’s Test by 0.000 (< 0.05) shows that there is a significant correlation between the indicators in this variable. This means the assumption sphericity fulfilled, and the data is worth exploring further. Table 12. KMO-MSA Values Anti-image Matrices X3.1 X3.2 X3.3 X3.4 X3.5 Anti-image Covariance X3.1 .118 -.014 -.007 .042 -.023 X3.2 -.014 .025 -.023 -.017 -.018 X3.3 -.007 -.023 .032 .000 -.001 X3.4 .042 -.017 .000 .916 .008 X3.5 -.023 -.018 -.001 .008 .118 Anti-image X3.1 .952a -.255 -.114 .127 -.191 Factor Matrixa Factor 1 X2.1 .956 X2.2 .935 X2.3 .934 X2.4 .912 X2.5 .940 X2.6 .925 Extraction Method: Principal Axis Factoring. Communalities Initial Extractio n X2.1 .889 .914 X2.2 .857 .875 X2.3 .849 .873 X2.4 .813 .831 X2.5 .861 .883 X2.6 .834 .856 Extraction Method: Principal Axis Factoring. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .855 Bartlett's Test of Sphericity Approx. Chi-Square 1541.188 df 10 Itself. .000 The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 684 Correlation X3.2 -.255 .764a -.818 -.111 -.338 X3.3 -.114 -.818 .801a .003 -.015 X3.4 .127 -.111 .003 .881a .026 X3.5 -.191 -.338 -.015 .026 .945a a. Measures of Sampling Adequacy(MSA) Source : Data Processed The results of the MSA test on each indicator showed values above 0.70, such as X3.1 (0.952), X3.2 (0.746), X3.3 (0.801), X3.4 (0.881), and X3.5 (0.945). These values reinforce the finding that each indicator has excellent feasibility for inclusion in the factor analysis. Table 13. Nilai Total Variance Explained Source : Data Process ed I n the results of Total Variance Explained, the first factor had an eigenvalue of 3,877, and was able to explain 77,534% of the initial total variance. After the extraction process, even though the value is tururn, this factor is still able to explain above 70%, which is 75.919%, which means that all indicators in this variable concentrate to form one main factor. This shows that the dimension of Brand Image is one-dimensional and strong in forming a whole construct. Table 14. Comunalities and matrix values Source : Data Processed From the table Communalities, It can be seen that the extraction value of almost all indicators is above 0.50, which means that the contribution of each indicator to the formation of factors is excellent. The X3.2 indicator shows a high value of 0.992, Meanwhile, the results Factor Matrix also shows that all indicators have factor loading which is quite strong, where almost all indicators reach more than 0.9, there is only one indicator that is below 0.9 but does not affect factor loading Overall. This reinforces that each indicator has a fairly high contribution strength to the constructed construct. Total Variance Explained Factor Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 3.877 77.534 77.534 3.796 75.919 75.919 2 .928 18.565 96.098 3 .100 2.000 98.099 4 .080 1.593 99.692 5 .015 .308 100.000 Extraction Method: Principal Axis Factoring. Factor Matrixa Factor 1 X3.1 .941 X3.2 .996 X3.3 .985 X3.4 .239 X3.5 .944 Extraction Method: Principal Axis Factoring. Communalities Initial Extractio n X3.1 .882 .886 X3.2 .975 .992 X3.3 .968 .970 X3.4 .084 .057 X3.5 .882 .891 Extraction Method: Principal Axis Factoring. The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 685 Purchase Intent Table 15. KMO & Bartlett's Test Scores KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .928 Bartlett's Test of Sphericity Approx. Chi-Square 1666.519 df 10 Itself. .000 Processed Data Sources Based on the results of the data processing, the value of the KMO for the Purchase Intention variable is 0.928, which indicates that the data has excellent sample sufficiency. The closer it is to number 1, the more suitable the data is to be analyzed using factor analysis. In addition, the significance value of Bartlett’s Test by 0.000 (< 0.05) shows that there is a significant correlation between the indicators in this variable. This means the assumption sphericity fulfilled, and the data is worth exploring further. Table 16. Values of SMEs-MSA Anti-image Matrices Y1.1 Y1.2 Y1.3 Y1.4 Y1.5 Anti-image Covariance Y1.1 .089 -.024 -.031 -.015 -.024 Y1.2 -.024 .111 -.031 -.023 -.010 Y1.3 -.031 -.031 .068 -.021 -.027 Y1.4 -.015 -.023 -.021 .148 -.030 Y1.5 -.024 -.010 -.027 -.030 .118 Anti-image Correlation Y1.1 .920a -.246 -.399 -.134 -.235 Y1.2 -.246 .936a -.353 -.178 -.090 Y1.3 -.399 -.353 .891a -.214 -.301 Y1.4 -.134 -.178 -.214 .956a -.230 Y1.5 -.235 -.090 -.301 -.230 .941a a. Measures of Sampling Adequacy(MSA) Source : Data Processed The results of the MSA test on each indicator show values above 0.80, such as X3.1 (0.920), X3.2 (0.936), X3.3 (0.891), Y1.4 (0.956), and Y1.5 (0.941). These values reinforce the finding that each indicator has excellent feasibility for inclusion in the factor analysis. Tabel 17. Nilai Total Variance Explained Source : Data Processed In the results of Total Variance Explained, the first factor has an eigenvalue of 4,645, and is able to explain 92,906% of the initial total variance. After the extraction process, even Total Variance Explained Factor Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 4.64 5 92.906 92.906 4.557 91.148 91.148 2 .121 2.427 95.334 3 .105 2.097 97.431 4 .076 1.522 98.953 5 .052 1.047 100.000 Extraction Method: Principal Axis Factoring. The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 686 though the value is tururn, this factor is still able to explain above 90%, which is 91,148%, which means that all indicators in this variable concentrate to form one main factor. This shows that the Buying Intent dimension is one-dimensional and very strong in forming a whole construct. Table 18. Values of Comunalities and matrices Source : Data Processed From the table Communalities, It can be seen that the extraction value of all indicators is above 0.50, which means that the contribution of each indicator to the formation of factors is excellent. Indicator Y1.3 shows the highest value of 0.914, the average indicator is above 0.8, Meanwhile, the results Factor Matrix It also shows that the whole indicator has a strong loading factor, where all indicators reach more than 0.9, This reinforces that each item has a high contribution force to the constructed construct. Classic Assumption Test Residual Normality Test The residual normality test aims to find out whether the residue (residual) of the regression model is normally distributed. This is important because the basic assumption in classical linear regression requires that the residual must be normally distributed for the estimation results to be valid. In this study, the test was carried out by looking at the residual histogram. If the histogram pattern is close to the normal curve (bell shape), then it can be concluded that the residual distribution meets the assumption of normality. The test results showed a distribution that was close to a symmetrical shape, so the model was declared to meet the assumption of normality. Communalities Initial Extractio n Y1.1 .911 .929 Y1.2 .889 .904 Y1.3 .932 .955 Y1.4 .852 .870 Y1.5 .882 .899 Extraction Method: Principal Axis Factoring. Factor Matrixa Factor 1 Y1.1 .964 Y1.2 .951 Y1.3 .977 Y1.4 .933 Y1.5 .948 Extraction Method: Principal Axis Factoring. The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 687 Figure 1. Residual Normality Test Source : Data Processed Based on the histogram results in the image above, it can be concluded that the residual distribution is centered around the zero value and shows a pattern that resembles a normal or bell-shaped curve. Although there are slight deviations in the tail, the overall distribution of the data still shows a symmetrical tendency. The residual mean value of - 4.16E-17 which is very close to zero, as well as the standard deviation of 0.993 indicate that the residual spread is quite stable. With a sample of 203 respondents, this provides support that residual data meets the assumption of normality. Therefore, it can be concluded that the data in this study have met the requirements of residual normality so that the linear regression model can be validly used for further analysis Multicollinearity Test The multicollinearity test was used to detect whether there is a strong linear relationship between independent variables in the regression model. Multicollinearity can interfere with the accuracy of the analysis results. A model is said to be free of multicollinearity if the VIF value is < 10, and vice versa, if the VIF is > 10 then an indication of multicollinearity occurs. Table 19. Multicollinearity Test Coefficientsa Model Unstandardized Coefficients Standardized Coefficients T Itself. Collinearity Statistics B Std. Error Beta Tolera nce BRIG HT 1 (Constant) -4,370E- 18 .036 .000 1.000 Quality of Service .370 .071 .363 5.230 .000 .276 3.621 Price .391 .092 .390 4.241 .000 .157 6.366 Brand Image .148 .082 .149 1.813 .071 .196 5.094 a. Dependent Variable: Purchase Intention (Choosing Guidance) Source : Data Processed The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 688 Based on the results of the output Coefficients in the table above, a multicollinearity test analysis can be carried out by looking at the Tolerance and Variance Inflation Factor (VIF) values for each independent variable, namely Service Quality, Price, and Brand Image. In detail, the Quality of Service has a Tolerance value of 0.276 and VIF of 3,621, the Price has a Tolerance value of 0.157 and VIF of 6,366, and the Brand Image has a Tolerance value of 0.196 and VIF of 5,094. Since all VIF values are < 10 and Tolerance values are > 0.10, it can be concluded that there is no multicollinearity among the independent variables in this regression model. Thus, the model is feasible to use in further analysis. Heteroscedasticity Test The heteroscedasticity test was performed to determine whether there was a residual variance disparity between observations in the regression model. If the points on the graph are randomly spread above and below the Y axis without forming a specific pattern, then heteroscedasticity does not occur. Based on the SPSS output graph, it can be concluded that the regression model in this study is free from heteroscedasticity problems. Figure 2. Heteroskedastista Test Source : Data Processed Based on the scatterplot graph of the heteroscedasticity test results above, it can be seen that residual points are randomly scattered above and below the zero horizontal line without forming a specific pattern, such as curved, narrowing, or widening lines. The absence of a clear pattern suggests that the variance of the residual is constant at each level of independent variable prediction, which is characteristic of data that do not experience heteroscedasticity. This unpatterned and random scattering of points is an important indicator that the classical assumption of homogeneity is fulfilled, so that there is no violation of one of the important assumptions in classical linear regression analysis. This gives confidence that the resulting regression coefficient estimation is unbiased and efficient. Thus, the regression model built can be said to be valid and can be continued to the stage of further interpretation of the results of regression analysis. Hypothesis Test The simultaneous F-test was used to test whether independent variables (service quality, price, and brand image) together had a significant effect on students' purchase intention in choosing tutoring. The results of the F test showed an F-count value of 184,523, greater than the F-table of 2.65, with a significance of 0.000, which means a significant The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 689 regression model. Meanwhile, the partial t-test showed that the quality of service and price had a significant influence on purchase intent, while brand image did not have a significant influence. The resulting multiple linear regression model is Y = -0.000 + 0.370X₁ + 0.391X₂ + 0.148X₃, which indicates that the quality of service and price significantly affect purchase intent, while brand image does not. A coefficient of determination (R²) of 0.736 indicates that 73.6% of the variability of buying intent can be explained by all three independent variables, with the remaining 26.4% explained by other factors outside of this model. Discussion Analysis This research was conducted to answer the need for a deeper understanding of the factors that affect students' purchase intention in choosing a tutoring institution, especially among high school XI students in the city of Bandung. In the increasingly competitive social reality of education, tutoring institutions are required not only to prioritize academic content, but also service aspects, pricing strategies, and brand imagery. Therefore, this analysis not only highlights the statistical relationships between variables, but also interprets the theoretical and applicable meanings of the results obtained. The following is a further discussion of the influence of each independent variable on the dependent variable: The Effect of Service Quality on Purchase Intention (Intention to Choose Guidance) The results of the regression analysis showed that the quality of service had a positive and significant influence on purchase intention, with a coefficient value of 0.370 and a significance level of 0.000. This indicates that the better the students' perception of the services provided by the tutoring institution such as punctuality in the delivery of material, tutor competence, clarity of the learning system, and interpersonal approach, the greater their tendency to choose the tutoring. These findings are in line with the SERVQUAL theory developed by Parasuraman et al. (1985), which states that service quality can be measured from five main dimensions: tangible (physical evidence such as facilities and equipment), reliability (service reliability), responsiveness (responsiveness to student needs), assurance (guarantee and trust), and empathy (attention to individual needs). In the context of tutoring, the quality of service is the main foundation that forms initial satisfaction and strengthens the student's intention to continue or recommend the service. In practical terms, this sends a message to tutoring managers that their success does not only depend on academic content, but also on how services are delivered holistically and professionally. Comfortable interactions, communicative tutors, and a neat learning system are aspects that are perceived and assessed by students as "plus value". The Effect of Price Quality on Buying Intention (Intention to Choose Guidance) The regression results also show that the price variable has a positive and significant effect on buying intention, with a coefficient of 0.391 and a significance level of 0.000. This means that the more in line with the student's perception of the price set with the benefits they feel, the higher their intention to choose the tutoring. These findings support the theory from Kotler and Keller (2016) that consumers' perception of price reflects perceived value, which is the perception of a balance between what is given and what is received. Prices are no longer understood absolutely as "expensive" or "cheap", but as prices that are decent, fair, and in accordance with the quality and results offered. In the context of grade XI students, who may be driven by personal or family considerations, cost efficiency and satisfactory learning outcomes are an important combination. Managerially, these results emphasize the need for adaptive pricing strategies. Tutoring that overly pressures the price without paying attention to quality will be considered unprofessional, while those that offer high prices without proof of service excellence will be abandoned by consumers. Seasonal discount strategies, learning package systems, or installments can also be an added value to strengthen students' purchase intentions. The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 690 The Influence of Brand Image on Purchase Intention (Intention to Choose Guidance) In contrast to the previous two variables, the results of the analysis showed that brand image had no significant effect on purchase intent, with a coefficient of 0.148 and a significance of 0.071 (> 0.05). Although it shows a positive influence direction, this relationship is not strong enough to be statistically influential in this model. In the theory of Brand Equity by Aaker (1996) and Customer-Based Brand Equity by Keller (2003), brand image is believed to be able to influence loyalty and purchasing decisions through positive brand associations. However, in the context of tutoring and respondents who were high school students in grade XI, these results reflect that they tend to make decisions based on tangible results and immediate benefits, rather than just on popularity or brand reputation. This indicates that the brand image has not been sufficiently embedded in the minds of adolescent consumers in choosing additional educational institutions. Another possibility is the low brand differentiation between tutors, making it difficult for students to distinguish one brand from another emotionally. So, branding investment in the long term remains important, but it is not the main determinant in the short term. The Simultaneous Influence of Service Quality, Price, and Brand Image on Purchase Intent The results of the F test showed that simultaneously, the three independent variables together had a significant effect on buying intent (F-count = 130.331 > F-table = 2.65; sig = 0.000). This reinforces that the linear regression model used in this study is feasible and statistically valid. This means that if all three variables—service quality, price, and brand image—are improved simultaneously, the potential for increasing student purchase intent will be even greater. This simultaneous influence confirms the importance of synergy between marketing variables, because although the brand image is not partially significant, but in combination with the other two variables, it still contributes to the overall model. This provides a strategic understanding that it is not enough for educational services companies to focus on just one aspect, but must maintain consistency and integration between quality services, competitive price quotes, and the institution's image in a sustainable manner. CONCLUSION The research found that service quality and price had a positive and significant influence on the purchase intention of high school XI students in choosing a guidance institution in Bandung, while brand image did not show a significant effect, indicating that students prioritized direct experience over institutional reputation. Together, these three variables explained 73.6% of the variation in students' purchase intentions. For management, it is important for tutoring institutions to focus on enhancing service quality and setting prices that reflect the value provided, while still making efforts to build brand loyalty and differentiation. Theoretically, these findings contribute to the service marketing literature in the non-formal education sector. Future research should consider involving parents as decision-makers and exploring additional variables such as learning experiences, social media, and social factors. Practically, institutions are encouraged to develop strategies that engage both students and parents, using a dual approach to improve purchasing decisions. REFERENCES Budiarti, A. (2021). The influence of promotion and product quality on purchase intention mediated by brand image. Journal of Economics and Business, 14(3), 233–245. Gisbert, D. D., & Rivas, A. V. (2021). Implementing peer tutoring for the development of empathy. Investigación y Educación en Enfermería, 39(2). https://doi.org/10.17533/udea.iee.v39n2e07 https://doi.org/10.17533/udea.iee.v39n2e07 The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 691 Hrastinski, S., Cleveland-Innes, M., & Stenbom, S. (2018). Tutoring online tutors: Using digital badges to encourage the development of online tutoring skills. British Journal of Educational Technology, 49(1), 127–136. https://doi.org/10.1111/bjet.12525 Kaniawati, K. (2021). Marketing strategy model in improving marketing performance (Case study of central small industry areas of Central Cibaduyut footwears). Turkish Journal of Computer and Mathematics Education, 12(9), 2472–2481. Kee, T., Zhang, H., & King, R. B. (2024). An empirical study on immersive technology in synchronous hybrid learning in design education. International Journal of Technology and Design Education, 34(3), 843–864. https://doi.org/10.1007/s10798-023-09855-5 Kotler, P., & Keller, K. L. (2016). Marketing management (15th ed.). Pearson Education. Kurniawan, B., Meyliana, Warnars, H. L. H. S., & Suharjo, B. (2023). The development of intelligent tutoring system in the military schools. ASEAN Journal of Science and Engineering, 3(3), 409–418. https://doi.org/10.17509/ajse.v3i3.57917 Masalimova, A. R., Ryazanova, E. L., Tararina, L. I., Sokolova, E. G., Ikrennikova, Y. B., Efimushkina, S. V., & Shulga, T. I. (2021). Distance learning hybrid format for university students in post-pandemic perspective: Collaborative technologies aspect. Cypriot Journal of Educational Sciences, 16(1), 45–56. https://doi.org/10.18844/cjes.v16i1.5536 Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education, 57(4), 521–535. https://doi.org/10.1111/ejed.12527 Mulaudzi, M. A., Du Toit, A., & Golightly, A. (2023). Hybrid problem-based learning in technology teacher preparation: Giving students a voice in their learning process. Journal of Education (South Africa), 90, 109–124. https://doi.org/10.17159/2520- 9868/i90a07 Newar, D. K. P., Zhao, R., Siy, H., Soh, L. K., & Song, M. (2023). SSDTutor: A feedback- driven intelligent tutoring system for secure software development. Science of Computer Programming, 227, 102933. https://doi.org/10.1016/j.scico.2023.102933 Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 62(1), 12–40. Patacsil, F. F., & Tablatin, C. L. S. (2017). Exploring the importance of soft and hard skills as perceived by IT internship students and industry: A gap analysis. Journal of Technology and Science Education, 7(3), 347–368. https://doi.org/10.3926/jotse.271 Sarwendah, A. P., Azizah, N., & Mumpuniarti, M. (2023). The use of technology in hybrid learning for students with special needs. Journal of Education and Learning, 17(2), 231–238. https://doi.org/10.11591/edulearn.v17i2.20810 Seraj, S. (2023). The challenges of personal tutoring in higher education: Applying a coaching approach at a UK higher education institution. International Journal of Evidence Based Coaching and Mentoring, 21(1), 51–67. Suryani, D. (2021). The role of technology in education: Perspectives on the quality of education in Indonesia. Journal of Education and Technology, 11(2), 67–80. Tjiptono, F. (2019). Service marketing: Principles, application, and research (4th ed.). Andi Publishers. Yusuf, S. (2022). Competitive strategies in the business of educational institutions: A case study on the Grissham hybrid course. Journal of Economics and Education, 17(3), 101– 115. https://doi.org/10.1111/bjet.12525 https://doi.org/10.1007/s10798-023-09855-5 https://doi.org/10.17509/ajse.v3i3.57917 https://doi.org/10.18844/cjes.v16i1.5536 https://doi.org/10.1111/ejed.12527 https://doi.org/10.17159/2520-9868/i90a07 https://doi.org/10.17159/2520-9868/i90a07 https://doi.org/10.1016/j.scico.2023.102933 https://doi.org/10.11591/edulearn.v17i2.20810 The Influence of Service Quality, Price, and Brand Image on the Purchase Intention of Grade XI High School Students in Choosing Tutoring in the City of Bandung 692 Copyright holders: Agus Sulistyo Budi, Ronny Samsul Bahri (2025) First publication right: AJEMB – American Journal of Economic and Management Business