Service quality can be defined as any form of activity carried out by a company in order to meet customer expectations (Gulo et al., 2022). In addition to this definition, service quality can also be interpreted through the emotional response of customers—whether satisfaction or disappointment—arising from the gap between their expectations and the actual performance of the product or service they receive. Thus, service quality is closely linked to subjective perceptions and varies depending on the individual experiences of customers.

According to Apriliana and Sukaris (2022), service quality consists of five core dimensions that help measure how well service providers meet consumer expectations. These include assurance, which reflects the competence and courtesy of employees in inspiring trust and confidence; reliability, which denotes the ability to perform the promised service dependably and accurately; empathy, which involves providing caring and individualized attention to customers; responsiveness, defined as the willingness to help customers and provide prompt service; and tangibles, referring to the physical facilities, equipment, and appearance of personnel. These dimensions collectively form a comprehensive framework that organizations can use to evaluate and improve their service delivery performance.

According to Kotler (as cited in Lieyanto, n.d.), product quality refers to the ability of a product to meet or exceed customer expectations. The quality of a product plays a crucial role in determining sales performance. The higher the quality, the better the company's reputation and customer satisfaction. Product quality is often seen as an essential competitive advantage in the marketplace, as it directly impacts consumer trust and loyalty.

Kotler, as referenced by Rivaldy and Rulirianto (2021), outlines several key dimensions of product quality. These include performance, which relates to the product's primary characteristics and its ability to perform as expected by the consumer. Consistency is another important dimension, emphasizing the reliability of the product’s performance over time and across different usage conditions. A product that consistently delivers on its promises is more likely to create strong, long-lasting customer relationships.

Timeliness refers to the concept that a delivery process must adhere to the specified date and time as promised (Tania & Raymond, n.d.). The estimated delivery time is an indicator of how well the logistics service provider performs in meeting customer expectations regarding delivery speed and accuracy (Hafizha et al., 2019). Timeliness in delivery is critical to customer satisfaction, as delays can lead to dissatisfaction and damage customer trust in the service provider.

According to Handoko, as cited in Eviani and Hidayat (2021), there are two key dimensions of timeliness in the context of logistics and delivery. The first dimension is the accuracy of the arrival time of goods, which means that the goods must arrive on the exact date and time as scheduled. The second dimension focuses on the timely delivery of goods, ensuring that the items are dispatched on time to meet the expected arrival window. Both dimensions are essential to ensure that logistics operations contribute positively to customer satisfaction.

Customer satisfaction is defined as the fulfillment of customer expectations by a company. It represents how well a product or service meets the needs, desires, and expectations of customers. According to Kotler, as cited in Ismail et al. (2021), there are several dimensions of customer satisfaction that help evaluate and understand how customers perceive their experiences with a service or product.

These dimensions include overall customer satisfaction, which refers to the general level of contentment with a product or service after consumption or use. The second dimension is the specific aspects of customer satisfaction, which break down the satisfaction into individual factors such as product quality, service delivery, and interaction quality. Lastly, expectation confirmation is another crucial dimension, which involves comparing the customer's initial expectations with the actual service or product received. If the product or service exceeds expectations, satisfaction tends to be higher.

Normality testing is performed to determine whether the data distribution is normal or not, as conducting multiple linear regression tests requires the data to be normally distributed. The hypothesis for the normality test is as follows: if the Sig value > 0.05, the data is normally distributed, while if the Sig value < 0.05, the data is not normally distributed. In the normality test conducted using SPSS, the obtained Sig value was 0.200 > 0.05, indicating that the data distribution is normal.

The linearity test is conducted to determine whether a significant linear relationship exists between two variables. A linear relationship is expected between the independent and dependent variables to achieve a good correlation. The hypothesis for this test is as follows: if the Sig value > 0.05, the data is linear, while if the Sig value < 0.05, the data is not linear.

In the linearity test, the Sig values obtained were 0.421 > 0.05 for Service Quality, 0.374 > 0.05 for Product Quality, and 0.364 > 0.05 for Timeliness, meaning there is a linear correlation between the independent and dependent variables.

The multicollinearity test is used to check whether there is a strong correlation among the independent variables in the regression model. High correlation among independent variables can lead to inaccurate or biased model estimates. Therefore, a good regression model should have independent variables that are not correlated or multicollinear. This test is typically performed by measuring the Variance Inflation Factor (VIF) and tolerance coefficient to assess the level of collinearity between variables. The hypothesis for this test is as follows: if the tolerance value > 0.10, no multicollinearity occurs; if the tolerance value < 0.10, multicollinearity occurs. For the tolerance test, the obtained values were Service Quality: 0.630 > 0.10, Product Quality: 0.620 > 0.10, Timeliness: 0.980

> 0.10

For the VIF hypothesis test, the following applies: if the VIF value < 10, no multicollinearity occurs; if the VIF value > 10, multicollinearity occurs. The VIF values obtained were Service Quality: 1.587 < 10, Product Quality: 1.612 < 10, Timeliness: 1.021 < 10. Since the obtained values meet the hypothesis requirements, it can be concluded that no multicollinearity exists.

The heteroscedasticity test is used to check whether the residual variance in the regression model is constant or varies. If the residual variance is constant, the model is said to experience homoscedasticity, while if it varies, the model experiences heteroscedasticity. Heteroscedasticity can reduce the accuracy of model estimates, so this test is important to perform.

The hypothesis for this test is as follows: if the Sig value > 0.05, heteroscedasticity does not occur; if the Sig value < 0.05, heteroscedasticity occurs. The obtained Sig values were Service Quality: 0.087 < 0.05, Product Quality: 0.087 < 0.05, Timeliness: 0.351 > 0.05, Since the points do not follow any pattern in the spread, it can be concluded that no heteroscedasticity is present. Since all classical assumption tests have met the requirements, the data in this study can proceed to the next test, which is multiple linear regression.

Table 2. Multiple Linear Regression Results

Based on the table above, the constant value obtained is 6.467, service quality (X1) is 0.211, product quality (X2) is 0.406, and timeliness (X3) is 0.407. Thus, the multiple linear regression model is as follows: Y = 6.467 + 0.211X1 + 0.406X2 + 0.407X3

The partial T-test was conducted to determine the individual (partial) effects of service quality, product quality, and timeliness on customer satisfaction. The hypothesis for the T-test is as follows: if the Sig value is less than 0.05, it indicates that service quality, product quality, or timeliness has a significant effect on customer satisfaction. Conversely, if the Sig value is greater than 0.05, it indicates that there is no significant effect on customer satisfaction.

The results of the partial T-test are as follows: the Sig value for service quality is 0.033 < 0.05, indicating a significant effect on customer satisfaction. The Sig value for product quality is 0.002 < 0.05, also indicating a significant effect. Lastly, the Sig value for timeliness is 0.046 < 0.05, which also indicates a significant effect of timeliness on customer satisfaction.

The simultaneous F-test was conducted to examine the combined effect of service quality, product quality, and timeliness on customer satisfaction. The hypothesis for the F-test is as follows: if the Sig value is less than 0.05, it indicates that service quality, product quality, and timeliness collectively have a significant effect on customer satisfaction. Conversely, if the Sig value is greater than 0.05, it indicates that there is no significant combined effect of these variables on customer satisfaction.

The test result shows a Sig value of 0.000 < 0.05, which means that service quality, product quality, and timeliness together have a significant effect on customer satisfaction.

The R square test yielded a value of 0.211, or 21%, which indicates that service quality, product quality, and timeliness collectively contribute directly to customer satisfaction. The remaining 79% is influenced by external variables not included in the model, which affect customer satisfaction.

In this study, service quality has a significant effect on customer satisfaction, with a Sig value of 0.033 < 0.05 from the partial T-test. This means that service quality impacts customer satisfaction. This finding aligns with the research conducted by Lay & Marvianta in their study titled "The Effect of Service Quality, Electronic Word Of Mouth (E-WOM), and Brand Image on Customer Satisfaction and Loyalty in Marketplace Application Users" (Lay & Marvianta, 2022).

In this study, product quality has a significant effect on customer satisfaction, with a Sig value of 0.001 < 0.002 from the partial T-test. This means that product quality impacts customer satisfaction. This finding is consistent with the research by Leonardo et al. in the study titled "The Effect of Product Quality, Promotion, and Price on Customer Satisfaction in Shopee Marketplace During the COVID-19 Pandemic (Study on Students of the Faculty of Economics and Business, Unsrat)" (Leonardo et al., 2021).

In this study, timeliness does not have a significant effect on customer satisfaction, with a Sig value of 0.046 > 0.05 from the partial T-test. This means that timeliness has a significant effect on customer satisfaction. This finding is in line with the research conducted by Melisari et al. in their study titled "The Effect of Service Quality, Timeliness, and Trust in J\&T Express Services on Customer Satisfaction in Marketplace Shopping (Case Study on FEB Unisma Students)" (Melisari et al., 2021).