The Effect of Service Quality and Location on Customer Satisfaction at Kedai 99 in Kaur Regency - Adisi Putra; Nurzam Nurzam; Muhammad Rahman Febliansa (Innovative Business Management Journal, 2026)
The Customer satisfaction is one of the key indicators of success that every organization seeks to achieve. Several factors influence customer satisfaction, including service quality and location. The main issue examined in this study is the extent to which service quality and location affect customer satisfaction at Kedai 99, Kaur Regency. This study aims to determine the effect of service quality and location on customer satisfaction, both simultaneously and partially. The independent variables in this research are service quality (X1), which refers to reliability, responsiveness, assurance, empathy, and tangible evidence, and location (X2), which includes accessibility, visibility, traffic conditions, parking facilities, and expansion potential. The dependent variable is customer satisfaction (Y), which is measured through overall service satisfaction, willingness to recommend to others, and intention to reuse the service. This research is a quantitative study in which data collection is represented in numerical form. The population of this study consists of customers of Kedai 99 in Kaur Regency. The sampling technique used was non-probability sampling, with 100 consumers as the population. Based on the Slovin formula, 80 respondents were selected as samples to test the relationships among variables. The results of the study indicate that service quality has a significant effect on customer satisfaction at Kedai 99, Kaur Regency. Based on the t-test results, the significance value obtained was 0.000, which is smaller than 0.05, indicating that the hypothesis stating service quality significantly affects customer satisfaction is accepted. Furthermore, the coefficient of determination test shows a value of 0.928 or 92.8%, meaning that service quality explains 92.8% of the variation in customer satisfaction, while the remaining 7.2% is influenced by other variables outside the regression model.