The Effect of Perceived Price and Product Quality on Dutch Decisions on Cumart Digital Applications with Customer Trust as Moderating Variables

. The Effect of Perceived


INTRODUCTION
Price is a major factor in purchasing decisions (Ma et al., 2022). Price shows the strategic function of the company (Penman & Zhu, 2022). If the price is too high, then the product is not affordable by certain target markets or the customer value is low S. Zhang et al., 2022). On the other hand, if the price is too low, the company will find it difficult to make a profit, or some consumers perceive that the quality is poor (Restiandi, 2020, p. 5) . So far, the product price range on the Cumart application is relatively affordable for the public. It's just that the shipping costs charged to consumers can change if the package during the weighing process is affected by a volume load. Cumart Customer Service will provide information to consumers if there are additional shipping costs due to the volume burden of the expedition.
Quality can be achieved if the company can provide products that meet and exceed consumer expectations (Anderson & Laverie, 2022). Companies that offer quality will build good relationships with consumers (Sun & Anwar, 2022). Good relationships that are created in the long term will enable companies to understand consumer needs (Li et al., 2022). A consumer will generally feel satisfied based on the experience of buying a product that is consumed or used (D. Zhang, 2022). Product quality plays an important role in influencing consumer satisfaction and consumer loyalty to the products offered by the company (Anggraeni et al., 2016, p. 172). Quality is a set of properties and characteristics of a product or service that depends on its ability to satisfy the needs expected by customers (Kotler & Keller, 2016).
1. Stability on a product 2. Habits in buying products 3. Give recommendations to others 4. Make a repeat purchase

Customer trust
Trust is one of the important factors that motivate customers to buy, especially when someone makes a purchase online. Without customer trust, transactions on online shopping sites will be more difficult to do (Gutavsson, 2006) in (Permana, 2020, p. 100).
According to (Salam, 2019) in (Permana, 2020)the customer trust indicator has 3 indicators, namely: 1. Honest in managing online business sites 2. Competence, where the site is attractive, easy and reliable to compete 3. The information provided is factual and can be trusted.

HYPOTHESIS
H1: There is an influence of price in shopping decisions on the digital meremart application H2: There is an influence of product quality in shopping decisions on the digital meremart application H3: Customer trust has the ability to strengthen or weaken the effect of price perception on shopping decisions in the digital application only.
H4: Customer trust has the ability to strengthen or weaken the influence of product quality on shopping decisions in the digital application only.

METHOD
The population in this study are all consumers who have used the digital application only in Jakarta.
Based on APJII that internet users in Jakarta are 4.6% , this study uses a sample of all consumers who use the digital application only in North Jakarta. This type of research is quantitative research that uses accidental sampling technique. Accidental sampling is a sampling method based on randomness, and anyone who meets the researcher by chance can be a sample Based on the eating formula, the minimum limit value is taken, namely 5. This study has 24 statement items, so the size or number of samples is 5 x 24 = 120 people.
This study uses 4 variables. Price perception variable (X1) and product quality (X2) as independent variables. The shopping decision variable (Y) is the dependent variable and the customer trust variable (Z) is the moderating variable.
The scale technique used in this study uses a Likert scale variable with a scale of 1 to 5 with details on a scale of 1 which means strongly disagree, scale 2 means disagree, scale 3 means neutral, scale 4 means agree, and scale 5 means strongly agree.
In this study, researchers chose the use of structural data with PLS-SEM including validity and reliability testing with a Goodness-of-fit evaluation.

RESULT AND DISCUSSION
The validity test according to Igbaria et al in Wijayanto and Ghozali (2008) standard loading factor 0.5 and or 0.7 is declared valid. The estimated factor loading value is presented in the default regression weight model by interpreting the CR (Critical Ratio) value, the significant value (p) probability and the estimated value (estimate). CR value = parameter estimation value divided by standard error (SE) value, if the CR value 1.96 is said to be significant (Hidajat et al., 1945, p. 450) .
n=(5 sampai 10) x K   (2022) The results of the validity test that were carried out produced valid numbers for all the questions. When viewed from the results of Smart PLS data processing, the significance figures for each indicator are in accordance with the existing criteria and are valid.

Test Inner Mode R-Square
The structural model or inner model is estimated from the explained percentage variance, i.e., R2 for the dependent hidden structure, using Stone-Geisser measurements via a bootstrap procedure. The goodness-of-fit evaluation of the inner model is evaluated using the R-square for the dependent latent variable with the same interpretation as the regression (Bastian, 2014, p. 5).

Hypothesis test
Hypothesis testing was carried out using the Bootstrapping resampling method developed by Geisser & Stone (Ghoza li, 2014). The size of the significance of the hypothesis support can be used to compare the values of the T-table and T-statistics. If the T-statistic is higher than the Ttable value, it means that the hypothesis is supported or accepted.

Figure 5. Path Coefficient
Source: Research Data Processing Results (2022) The results of the path coefficient based on the T-Statistics value show that the effect of price perception on shopping decisions has a significance level of 0.944 so it is stated that the effect of price perceptions has no positive and significant effect on shopping decisions and the hypothesis is rejected. This is contrary to research conducted by Elien (2017) which shows that price perception has a positive effect on Alfamart customer purchasing decisions. These results are in accordance with the research of Sugiarto (2013) which shows that price perception has a significant effect on purchasing decisions (I'tishom, M. F., Martini, S., & Novandari, 2020).
The results of the path coefficient based on the T-Statistics value show that product quality has a significance level of 3,985 so it is stated that product quality has a positive and significant effect on shopping decisions and the hypothesis is accepted. The results of this study are in accordance with previous research by Shareef et al. (2008), argues that product quality has a significant positive effect on purchasing decisions. This statement is supported by Shaharudin et al. (2011), Ackaradejruangsri (2013, Tamunu and Ferdinand (2014) and Kalicharan (2014) (Oktavenia & Ardani, 2018).
The results of the path coefficient based on the T-Statistics value show that the relationship between customer trust and shopping decisions has a significance level of 3,166 so it can be stated that customer trust has the ability to strengthen shopping decisions and the hypothesis is accepted. The results of this study are in accordance with the research conducted by Chen et al. (2014) found that the expression of trust strengthens the relationship between perceived risk and satisfaction, where the interaction of gender and trust plays an important role in the influence of perceived benefits on online purchase intentions (Dessyaningrum et al., 2020, p. 444).
The results of the path coefficient based on the T-Statistics value indicate that customer trust is not able to mediate price perceptions on shopping decisions because it has a significance level of 0.340 so that the hypothesis is rejected.
The results of the path coefficient based on the T-Statistics value indicate that customer trust is not able to mediate product quality on shopping decisions because it has a significance level of 0.391 so that the hypothesis is rejected.

CONCLUSION
The results of hypothesis testing in the table above show that the relationship between the effect of price perception (X1) and shopping decisions (Y) is not significant with T-Statistic < 1.96 which is 0.944 , which means that based on this the hypothesis in this study is rejected.
The results of hypothesis testing in the table above show that the relationship between product quality (X2) and shopping decisions (Y) is significant with T-Statistic < 1.96 which is 3.985 , which means that based on this the hypothesis in this study is accepted.
The results of hypothesis testing in the table above show that the relationship between customer trust (Z) and shopping decisions (Y) is significant with T-Statistics > 1,96 which is 3,166, which means that based on this the hypothesis in this study is accepted.
The results of hypothesis testing based on the table above show that the relationship between customer trust as a moderating variable of price perception is not significant to shopping decisions with T-Statistic < 1.96 which is 0.340 which means that based on this the hypothesis in this study is rejected.
The results of hypothesis testing based on the table above show that the relationship between customer trust as a moderating variable of product quality is not significant to shopping decisions with T-Statistics < 1,96 which is 0.391 which means that based on this research the hypothesis in this study is rejected.

Limitations
The limitations of this study are the research subjects whose scope is only the community of users of the Cumart digital application in the North Jakarta area and also this research was carried out in the midst of the Covid-19 pandemic outbreak and restrictions on community activities by the DKI Jakarta government. For future research, it is not limited to one city area or a wider area so that it can provide a broader picture in data analysis.