Influence of Rate and Facilities on User Satisfaction of Damri Transportation Services in North Central Timor District - Yesus Armiro Korbaffo; Aquidowaris Manek; Nurul Huda; Yeremias Lake; Emanuel Tati Taena (Budapest International Research and Critics University, 2022)
The problems raised in this study are: Does the tariff (X1) and whether the facilities (X2) affect the satisfaction of Damri transportation service users in Kab. TTU? The aim of this study is to determine the effect of tariffs (X1) and the effect of facilities (X2) on service user satisfaction (Y) at Perum Damri in TTU Regency partially and simultaneously. The sample used in this study is a saturated sample that uses all of the population as a sample of 100 people. Data collection methods used are questionnaires, interviews, observation and documentation. The data analysis technique used is simple linear regression analysis technique and multiple linear regression. The results of simple regression analysis between the Tariff variable (X1) on Service User Satisfaction (Y) are: Y = 5.954 + 0.600 X2 + i. The R2 value obtained from this study is 0.533, meaning that the percentage contribution of the influence of the Tariff variable (X1) on Service User Satisfaction (Y) in this research model is very small (less than 50%). The results of the facility variable (X2) on Service User Satisfaction (Y) are: Y = 7.288 + 0.597 X3 + i. The R2 value obtained from this study is 0.615, meaning that the percentage contribution of the influence of the Facility variable (X2) on Service User Satisfaction (Y) in this research model is quite small (less than 50%). The results of multiple linear regression analysis of rates (X1) and facilities (X2) on service user satisfaction (Y) are: Y = 2.152 + 0.248 (X1) + 0.362 (X2). The magnitude of the coefficient of determination or R2 of 0.492, it can be interpreted that 49.2% of the variation of the service user satisfaction variable at Damri in Kab. TTU can be explained by the Tariff (X1) and Facilities (X2) variables, while the rest (50.8%) is influenced by other variables outside the model.