1113 INVESTIGATING THE EFFECT OF MARKETING MIX THROUGH MOTIVATION ON PURCHASE INTENTION IN PERUMNAS GRIYA JETIS PERMAI MOJOKERTO Volume: 4 Number: 4 Page: 1113 - 1124 Atras Radifan PUSPITO1, Aldina SHIRATINA2 1,2Master of Management, Post Graduate Program, University of Mercu Buana, Jakarta, Indonesia Corresponding author: Atras Radifan Puspito E-mail: muhammad.puspito@gmail.com Article History: Received: 2023-06-28 Revised: 2023-07-03 Accepted: 2023-07-17 Abstract: This research examines the direct and indirect effects of Product, Price, Promotion, Place, and Motivation on Purchase Intention at Perumahan Griya Jetis Permai Mojokerto. The research sample consists of 255 respondents who are buyers that have gone through the marketing and sales process. The non- Probability Sampling method was used for data collection, employing a Likert scale questionnaire. The data analysis method used in this study is Partial Least Square-Structural Equation Modelling (PLS-SEM) through Outer Model, Inner Model, and Resampling Bootstrap. The study results show that Product, Price, Promotion, and Place positively and significantly influence Motivation. Motivation also has a positive and significant effect on Purchase Intention. Additionally, Product and Promotion positively and significantly impact Purchase Intention. However, Price and Place do not positively and significantly affect Purchase Intention. Overall, the research results indicate that Motivation plays a mediating role as an intervening variable between Product, Price, Promotion, and Place on Purchase Intention at Perumahan Griya Jetis Permai Mojokerto. The findings of this study can be used as a guide for Perumahan Griya Jetis Permai Mojokerto in designing effective marketing strategies to enhance Purchase Intention. Keywords: Marketing, Partial Least Square, Purchase Intention, Structural Equation Modeling Cite this as: PUSPITO, A.R., SHIRATINA, A. (2023). “Investigating the Effect of Marketing Mix Through Motivation on Purchase Intention in Perumnas Griya Jetis Permai Mojokerto," International Journal of Environmental, Sustainability, and Social Science, 4(4), 1113 - 1124. INTRODUCTION Houses are crucial in human life, serving as shelters and expressions of identity and comfort needs. In Indonesia, there is a housing shortage or housing backlog of 12.75 million units, which has the potential to continue increasing (Gofur & Jumiati, 2021). Moreover, the property sector has also been impacted by the Covid-19 pandemic, leading to a decline in people's income and property sales (Nasution et al., 2020). The prices of most properties, such as houses, apartments, and motor vehicles, have experienced a significant decrease. This is due to the reduced demand for properties as people become more cautious about their expenditures amid the pandemic (Nurpita & Wardani, 2021). Marketing management plays a crucial role in identifying and capitalizing on market opportunities. In this context, property sales in Indonesia have experienced an increase post- pandemic, significantly contributing to the GDP and surpassing pre-pandemic growth rates in the property sector (Magfirah & Habiburahman, 2022). The government has also supported stimulus policies and incentives to boost property sales. Perum Perumnas, a state-owned enterprise that provides housing for middle and lower-income communities, has the Perumnas Griya Jetis Permai mailto:muhammad.puspito@gmail.com 1114 project in Mojokerto Regency. However, house sales in this project have declined during and after the pandemic. Table 1. Data on Housing Sales in Mojokerto No. Housing Developer Housing Sale (unit) 2019 2020 2021 2022 2023* 1. Garden View Residence 50 57 77 78 27 2. The Khadefa Residence 77 71 80 80 32 3. Star Garden Residence 58 62 81 85 30 4. Zian Istana Residence 79 70 85 92 40 5. Bumi Mojopahit 65 60 78 89 33 6. Graha Permata Safir 90 75 105 107 43 7. Green Jayanegara 76 99 90 102 45 8. Perumnas Griya Jetis Permai 68 50 45 39 19 Note: *Data up to May 2023 Source: Compiled from various data sources (2023) After the Covid-19 pandemic subsided, there was an increase in home purchases, as shown in the competitor table above. The rise in house sales could be attributed to several factors, the dominant factor being the increase in consumer purchase intention (Li et al., 2022). Purchase intention refers to a customer's desire to buy a product, even though it does not guarantee an actual purchase. It is a behavioral response of consumers to a product that creates a desire to purchase. According to Kotler & Keller (2012), purchase intention represents the consumer's action after receiving product stimuli, which leads them to desire and intend to purchase and possess the product. This involves a positive attitude towards an object that motivates individuals to strive for it through payment or sacrifice. Various factors can influence purchase intention, and one of them is Motivation. Motivation is a factor that affects consumers' desire to buy or possess a product (Christiarini & Rahmadilla, 2021). Consumers have motivations to acquire products that align with their wants and needs. The marketing mix also influences consumer motivation. An optimal marketing mix strategy can affect consumer motivation in achieving their purchase goals (Akgün et al., 2014). The marketing mix, comprising product, price, promotion, and place/distribution, is used by companies to achieve their marketing objectives in the target market. By combining these elements, companies can increase the number of customers and sales and gain profits and a positive image (Londhe, 2014). In the context of Perumnas Griya Jetis Permai Mojokerto, the decrease in house sales can be related to an analysis of the 4P marketing mix, which is believed to influence Motivation as an intervening variable (Wichmann et al., 2022). Additionally, Motivation influences consumer purchase decisions when choosing a house positively. Considering these factors, this research examines the impact of the marketing mix through Motivation on purchase intention at Perumnas Griya Jetis Permai Mojokerto using the Partial Least Square-Structural Equation Modelling (PLS- SEM) method. By gaining a deeper understanding of these factors, it is expected to provide valuable insights for Perumnas Griya Jetis Permai Mojokerto in optimizing their marketing strategies to enhance consumer interest and purchase intent. This article consists of three parts. The second part presents the methodology and analytical steps, while the empirical results and conclusions are presented in the third part. 1115 METHODS The data used in this research is primary data obtained through direct surveys with the consumers of Perumnas Griya Jetis Permai, specifically those who have purchased a housing unit and have gone through all marketing and sales stages, starting from the booking of a unit (NUP), down payment (DP) payment, until the Credit Agreement execution. The sampling technique employed in this study utilizes the sampling method from Hair et al. (2011) with the following sampling calculation: N = 5 × operational variable research indicator If using five variables, with each variable divided into three dimensions and each dimension elaborated into three research indicators, the minimum number of respondents required for this study is N = 5 × 3 × 3 = 45. Therefore, a total of 45 respondents are needed for the sampling. However, the study involves six latent variables measured by 6 to 9 manifest variables (indicators). Table 2 summarizes all the variables examined and their relationships with the constructs under investigation. All indicators are measured on a Likert scale ranging from 1 to 5, where one indicates "strongly disagree," and five indicates "strongly agree." Table 2. Constructs and Indicators No Variable Indicator Item 1. Purchase Intention (Z) 1. Consumer Needs Q1 2. Product Information Q2 3. Product Benefits Q3 4. Consumer Trust in the Marketer Q4 5. Consumer Trust in the Product Q5 6. Consumer Trust in the Company Q6 7. Interest in Making Transactions Q7 8. Interest in Recommending or Referring Q8 9. Interest in Making it a Top Priority Q9 2. Motivation (Y) 1. Social Status Q10 2. Uniqueness Q11 3. Pride Q12 4. Ease of Purchase Q13 5. Improved Quality of Life Q14 6. Economic Value Q15 3. Product (X1) 1. Consistency of Product Quality Q16 2. Product Quality Meeting Expectations Q17 3. Good or Bad Reputation of the Product Q18 4. Product Design Meeting Functionality Q19 5. Product Design Meeting Expectations Q20 6. Product Design Becomes a Trend Q21 7. Adequate Supporting Facilities Q22 8. Sufficient Parking Area Q23 9. Convenient Shopping Places for Necessities Q24 4. Price (X2) 1. Competitive Pricing Q25 2. Pricing Reflecting Benefits Q26 3. Pricing Aligned with Value Q27 4. Quality Matching the Price Q28 1116 No Variable Indicator Item 5. Pricing According to Needs Q29 6. Pricing According to Desires Q30 7. Pricing in Line with Objectives Q31 8. Obtaining the Best Price Q32 9. Achieving the Targeted Profits Q33 5. Promotion (X3) 1. Advertising on Social Media Q43 2. Advertising on Television Media Q44 3. Advertising on Print Media Q45 4. Exhibitions Q46 5. Presentations Q47 6. Customer Relations Q48 7. Discounts Provided Q49 8. Events or Occasions Q50 9. Gifts Q51 6. Location (X4) 1. Accessible Location Q43 2. Availability of Transportation Facilities Q44 3. Wide Coverage Area Q45 4. Proximity to Residential Areas Q46 5. Nearness to Supporting Facilities Q47 6. Easy-to-Find Location Q48 7. Easy Toll Access Q49 8. Timeliness of Private and Public Transportation Q50 Figure 1. Idea Framework Next, a theoretical framework is constructed to illustrate the thought process developed in this research. The framework depicts the following flow. The hypotheses proposed by the author for this research are as follows: Direct Effect of the Latent Variable "Product" on "Motivation." H0: There is no direct positive effect of "Product" on "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a direct positive effect of "Product" on "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. Direct Effect of the Latent Variable "Price" on "Motivation." H0: There is no direct positive effect of "Price" on "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a direct positive effect of "Price" on "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. 1117 Direct Effect of the Latent Variable "Promotion" on "Motivation." H0: There is no direct positive effect of "Promotion" on "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a direct positive effect of "Promotion" on "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. Direct Effect of the Latent Variable "Place" on "Motivation." H0: There is no direct positive effect of "Place" on "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a direct positive effect of "Place" on "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. Direct Effect of the Latent Variable "Motivation" on "Purchase Intention." H0: There is no direct positive effect of "Motivation" on "Purchase Intention" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a direct positive effect of "Motivation" on "Purchase Intention" for Perumnas Griya Jetis Permai Mojokerto property. Direct Effect of the Latent Variable "Product" on "Purchase Intention." H0: There is no direct positive effect of "Product" on "Purchase Intention" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a direct positive effect of "Product" on "Purchase Intention" for Perumnas Griya Jetis Permai Mojokerto property. Direct Effect of the Latent Variable "Price" on "Purchase Intention." H0: There is no direct positive effect of "Price" on "Purchase Intention" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a direct positive effect of "Price" on "Purchase Intention" for Perumnas Griya Jetis Permai Mojokerto property. Direct Effect of the Latent Variable "Promotion" on "Purchase Intention." H0: There is no direct positive effect of "Promotion" on "Purchase Intention" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a direct positive effect of "Promotion" on "Purchase Intention" for Perumnas Griya Jetis Permai Mojokerto property. Direct Effect of the Latent Variable "Place" on "Purchase Intention." H0: There is no direct positive effect of "Place" on "Purchase Intention" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a direct positive effect of "Place" on "Purchase Intention" for Perumnas Griya Jetis Permai Mojokerto property. Indirect Effect of the Latent Variable "Product" on "Purchase Intention" through "Motivation." H0: There is no positive indirect effect of "Product" on "Purchase Intention" through "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a positive indirect effect of "Product" on "Purchase Intention" through "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. Indirect Effect of the Latent Variable "Price" on "Purchase Intention" through "Motivation." H0: There is no positive indirect effect of "Price" on "Purchase Intention" through "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a positive indirect effect of "Price" on "Purchase Intention" through "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. Indirect Effect of the Latent Variable "Promotion" on "Purchase Intention" through "Motivation." H0: There is no positive indirect effect of "Promotion" on "Purchase Intention" through "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a positive indirect effect of "Promotion" on "Purchase Intention" through "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. Indirect Effect of the Latent Variable "Place" on "Purchase Intention" through "Motivation." H0: There is no positive indirect effect of "Place" on "Purchase Intention" through "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. H1: There is a positive indirect effect of "Place" on "Purchase Intention" through "Motivation" for Perumnas Griya Jetis Permai Mojokerto property. 1118 This research uses PLS-SEM to analyze the influence of marketing mix on purchase motivation and purchase intention. Structural Equation Modeling (SEM) is a statistical method used to test and model complex relationships between variables in a study (Kline, 2016). Partial Least Squares Structural Equation Modeling (PLS-SEM) utilizes the principal component modeling approach to reduce the dimensionality of data and build a more straightforward and more interpretable model (Hair et al., 2011). The PLS-SEM analysis consists of evaluating the outer model and the inner model. The Outer Model evaluates the measured variables that are part of the SEM or PLS-SEM model. This evaluation involves testing the validity and reliability of constructs and selecting the most appropriate variables to represent the measured constructs. The validity testing in the outer model includes convergent validity and discriminant validity. Convergent validity is evaluated by examining how well the measured indicators contribute to measuring a specific construct. Several indicators represent a construct, and convergent validity is measured by assessing the strong positive correlations between these indicators and the same construct. One commonly used method to test convergent validity is by looking at the Average Variance Explained (AVE) value obtained through the following equation. AVE = ∑ λi 2n i=1 ∑ λi 2n i=1 + ∑ εi n i=1 Where λi represents the component loading to the indicator, and εi = 1 - λi, convergent validity is met if the AVE value is more significant than 0.5. Discriminant validity is the ability of a latent variable to be distinguished from other latent variables. Indicators of a latent variable should be more strongly related to their underlying latent variable and less related to other latent variables. Discriminant validity testing involves examining the cross-loading values, where indicators should have stronger correlations with their latent variable than with other latent variables. The cross-loading values should be > 0.7 within one variable, or the cross-loading value of an indicator measuring its latent variable should be higher than that with other latent variables. Reliability testing measures an indicator's consistency, accuracy, and precision in measurement. In SEM-PLS, reliability is tested using the Composite Reliability (CR) parameter, calculated using the following equation. An indicator is reliable if the CR value is more significant than 0.7. CR = (∑ λi n i=1 ) 2 (∑ λi n i=1 ) 2 + ∑ var(εi) n i=1 Composite Reliability (CR) describes the extent to which the indicators collectively obtain consistent information about the latent variable. CR values range from 0 to 1, with higher values indicating better reliability. In a study, CR values above 0.7 indicate that the used indicators are adequate. Composite Reliability Convergent (CRC) is a reliability metric based on the convergence or consistency among the indicators used to measure the latent variable. On the other hand, Composite Reliability Average (CRA) is a reliability metric that measures reliability based on the average loading factor of indicators across all latent variables in the model. The evaluation of the inner model is the process of assessing and testing the relationships between latent variables (constructs) in an SEM or PLS-SEM model. The evaluation aims to test the 1119 theoretical validity of the proposed hypotheses in the model and understand how well the model fits the available empirical data. RESULT AND DISCUSSION Perum Perumnas was established as a government solution to provide decent housing for lower and middle-income communities. With the enactment of Law No. 19 of 2003 concerning State- Owned Enterprises (BUMN), the establishment of Perum Perumnas was further perfected through Government Regulation No. 15 of 2004, dated May 10, 2004. Subsequently, the existence of Perum Perumnas was further refined with Government Regulation 83 of 2015 concerning the National Housing Development Public Company, which is a renewal of Government Regulation No. 15 of 2004. The issuance of Government Regulation No. 83 of 2015 has transformed Perum Perumnas into the National Housing & Urban Development Corporation. Figure 2. House Ownership Status Based on the survey data of Perumnas Griya Jetis Permai Mojokerto residents, 92% of the 250 respondents are homeowners, and the remaining 8% are renters. This percentage means that 231 respondents are homeowners and 19 are renters occupying houses in Perumnas Griya Jetis Permai Mojokerto. In the context of this research, one respondent represents one household in the housing complex. The results of the PLS-SEM analysis consist of evaluating the outer and inner models. The Measurement Model (Outer Model) measures latent variables or constructs observed through measured indicators. The Outer Model is evaluated based on the validity and reliability of the indicators used in the model. Validity testing aims to ensure that the instruments or indicators used in the research are reliable and accurate in measuring the studied variables. Several evaluation metrics that measure validity in SEM-PLS are Convergent and Discriminant Validity. 92% 8% Owner Tenant 1120 Figure 3. Model Result Figure 3 presents the loading factor values of each indicator on the latent variables. The loading factor values can also be seen in the following Table 3. Table 3. Loading Factor on Each Indicator Indicator X1 X2 X3 X4 Y Z X11 0.795 0.272 0.100 0.175 0.351 0.367 X12 0.735 0.296 0.193 0.198 0.428 0.431 X13 0.729 0.283 0.104 0.226 0.447 0.441 X14 0.731 0.257 0.076 0.144 0.336 0.324 X15 0.728 0.272 0.176 0.199 0.368 0.392 X16 0.747 0.197 0.190 0.193 0.333 0.381 X17 0.775 0.274 0.037 0.137 0.350 0.351 X18 0.673 0.231 0.020 0.085 0.276 0.260 X19 0.775 0.289 0.126 0.204 0.342 0.353 X21 0.222 0.743 0.296 0.272 0.518 0.457 X22 0.231 0.686 0.259 0.265 0.506 0.434 X23 0.233 0.741 0.270 0.259 0.507 0.474 X24 0.258 0.743 0.276 0.318 0.541 0.511 X25 0.228 0.709 0.295 0.158 0.457 0.398 X26 0.234 0.718 0.208 0.258 0.481 0.413 X27 0.289 0.685 0.212 0.216 0.452 0.454 X28 0.244 0.738 0.273 0.312 0.557 0.528 X29 0.373 0.752 0.220 0.262 0.583 0.523 1121 X31 0.113 0.324 0.783 0.138 0.375 0.393 X32 0.188 0.218 0.710 0.231 0.346 0.440 X33 0.140 0.242 0.749 0.188 0.352 0.416 X34 0.066 0.183 0.702 0.145 0.278 0.300 X35 0.134 0.255 0.723 0.125 0.281 0.296 X36 0.159 0.308 0.748 0.175 0.332 0.383 X37 0.119 0.288 0.679 0.171 0.297 0.357 X38 0.073 0.170 0.643 0.183 0.269 0.279 X39 0.003 0.283 0.729 0.215 0.302 0.327 X41 0.194 0.305 0.201 0.751 0.387 0.385 X42 0.152 0.263 0.223 0.736 0.384 0.373 X43 0.194 0.341 0.236 0.794 0.478 0.465 X44 0.246 0.275 0.225 0.731 0.445 0.421 X45 0.144 0.210 0.167 0.699 0.356 0.331 X46 0.192 0.203 0.131 0.721 0.343 0.294 X47 0.120 0.220 0.142 0.736 0.353 0.319 X48 0.150 0.264 0.082 0.717 0.390 0.359 Y1 0.339 0.563 0.384 0.403 0.793 0.721 Y2 0.431 0.604 0.383 0.435 0.821 0.750 Y3 0.366 0.564 0.298 0.408 0.750 0.676 Y4 0.380 0.486 0.267 0.424 0.783 0.690 Y5 0.385 0.554 0.305 0.390 0.795 0.708 Y6 0.397 0.549 0.424 0.462 0.744 0.721 Z1 0.412 0.546 0.462 0.359 0.724 0.793 Z2 0.446 0.515 0.319 0.398 0.718 0.774 Z3 0.363 0.465 0.252 0.416 0.671 0.725 Z4 0.417 0.464 0.415 0.348 0.680 0.762 Z5 0.401 0.471 0.232 0.436 0.664 0.695 Z6 0.343 0.455 0.379 0.435 0.646 0.726 Z7 0.296 0.442 0.341 0.346 0.653 0.693 Z8 0.241 0.447 0.427 0.370 0.618 0.704 Z9 0.418 0.515 0.506 0.305 0.715 0.809 Based on Table 3 and Figure 3, it can be observed that all indicators used to explain the latent variables are valid as they meet the criteria where the loading factor values are more significant than 0.6. Discriminant validity evaluates the extent to which different latent variables or constructs differ from one another and are not overly correlated. Discriminant validity is essential to distinguish different latent variables conceptually. Discriminant validity can be evaluated through the cross- loading of factors. Table 3 also shows that the correlation of each indicator with its corresponding latent variable is higher than the correlation with other latent variables. Thus, each indicator properly explains the latent variable and has good discriminant validity. Discriminant validity can also be measured from each latent variable's Average Variance Extracted (AVE) values. AVE is a metric used to measure the extent to which the indicators used to measure the latent variables contribute to the variability of the constructs they measure. Discriminant validity is considered adequate if the AVE value is more significant than 0.5. The AVE values for each latent variable are displayed in the following Table 4. Table 4. Average Variance Extracted (AVE) Variable Average Variance Extracted (AVE) Purchase Intention (Z) 0.610 1122 Buying Motivation (Y) 0.553 Product (X1) 0.553 Price (X2) 0.525 Promotion (X3) 0.518 Location (X4) 0.542 It can be seen from Table 4 that all latent variables have AVE values above 0.5. This means that the variables used in the model have adequate discriminant validity. Reliability testing on the outer model in PLS-SEM analysis is conducted to ensure that the indicators used to measure latent variables have sufficient reliability. Reliability testing provides information about the consistency of the indicators in measuring the same latent variable. Some metrics that can be used to test reliability in the outer model are Cronbach's Alpha and Composite Reliability. Table 5. Pengukuran Reliabilitas Outer Model Variable Cronbach's Alpha Composite Reliability (rho_a) Composite Reliability (rho_c) Purchase Intention (Z) 0.898 0.900 0.917 Buying Motivation (Y) 0.872 0.873 0.904 Product (X1) 0.899 0.903 0.918 Price (X2) 0.887 0.889 0.908 Promotion (X3) 0.884 0.888 0.906 Location (X4) 0.879 0.885 0.904 Cronbach's alpha is a commonly used metric to measure the internal reliability of measurement instruments. The value of Cronbach's alpha ranges from 0 to 1, and higher values indicate better reliability. A Cronbach's alpha value above 0.7 is acceptable in a research study. As shown in Table 4.3, Cronbach's alpha values for each variable are above 0.7, indicating that the used indicators have good reliability. Based on Table 5, the CRA and CRC values for each variable are above 0.7 and close to 1. Therefore, the indicators used have good reliability. Hypothesis testing using the Resampling Bootstrap method is a statistical technique commonly used in PLS-SEM analysis to test the significance of model parameters. In this study, there are 13 hypotheses, including nine direct effects and four indirect effects. Using a significance level of 5%, the following are the t-statistic and p-value values from the hypothesis testing for direct effects. If the p-value is less than the significance level, then the H0 is rejected. Table 6. Results of Hypothesis Testing for Direct Effects T-statistics P-values Product -> Buying Motivation 5.724 0.000 Product -> Purchase Intention 2.199 0.028 Price -> Buying Motivation 8.885 0.000 Price -> Purchase Intention 0.288 0.773 Promotion -> Buying Motivation 4.220 0.000 Promotion -> Purchase Intention 3.605 0.000 Place -> Buying Motivation 6.407 0.000 Place -> Purchase Intention 0.739 0.460 1123 Buying Motivation -> Purchase Intention 16.968 0.000 Based on the hypothesis testing using the Resampling Bootstrap method, it is evident that there is a direct positive influence of the variables Product, Price, Promotion, and Place on the Buying Motivation of Perumnas Griya Jetis Permai Mojokerto's properties. Moreover, Product, Place, and Buying Motivation significantly influence Purchase Intention. This is supported by the p- values being less than the significance level of 5%. Next, we will examine the indirect effects of the variables Product, Price, Promotion, and Place on Purchase Intention through the mediating variable, Buying Motivation, with the following results. Tabel 7. Hasil Pengujian Hipotesis Indirect Effect T-statistics P-values Price -> Buying Motivation -> Purchase Intention 8.764 0.000 Promotion -> Buying Motivation -> Purchase Intention 4.177 0.000 Product -> Buying Motivation -> Purchase Intention 4.989 0.000 Place -> Buying Motivation -> Purchase Intention 5.635 0.000 Based on the p-value in Table 7, which is less than 5%, it is known that the variables Product, Price, Promotion, and Place have a significant influence on Purchase Intention through Buying Motivation in the context of Perumnas Griya Jetis Permai Mojokerto. Several metrics can be used to evaluate the structural model in SEM PLS analysis, one of which is the coefficient of determination R². The coefficient of determination R² measures how well the structural model can explain the endogenous or dependent latent variable variation. R² values range from 0 to 1, and the higher the value, the better the model explains the variation in the endogenous variables. Table 8. Structural Model Evaluation R-square Adjusted R-square Buying Motivation 0.669 0.664 Purchase Intention 0.848 0.845 The R² value of 66.9% for the Buying Motivation variable indicates that 66.9% of the variability in the Buying Motivation variable can be explained by the exogenous (independent) variables in the model. Meanwhile, the R² value of 84.8% indicates that 84.8% of the variability in the Purchase Intention variable can be explained by the exogenous variables in the model. CONCLUSION Based on the evaluation of the outer model, it can be concluded that the indicators used to explain the latent variables have met the criteria of validity and reliability. 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