Influence of product quality, promotion and design on purchasing decisions for Yamaha Mio motorized vehicles
DOI:
https://doi.org/10.59651/digital.v14i1.53Keywords:
Buying Decision, Product Quality, Promotion, DesignAbstract
This study aims to analyze the influence of product quality, promotion and design on the purchasing decision of Yamaha Mio motorcycle products and to analyze the variables that have the most dominant influence on the purchasing decision of Yamaha Mio motorcycle products in the people of Surakarta. The population in this study were all consumers of Yamaha Mio motorbikes in the Surakarta area. The sample used was 150 respondents with the Accidental Quota Sampling technique. The data collection technique used a questionnaire, while the data analysis technique used multiple linear regression analysis, classical assumptions (normality test, multicollinearity test, heteroscedasticity test), statistical tests (t test, F test and coefficient of determination). From the results of the classical assumption analysis, the normality test with Kolmogorovsmirnov was obtained significantly greater than 0.05, which means that the data is normally distributed. The multicollinearity test obtained VIF and Tolerance values that were close to one so that it could be concluded that the regression model had no multicollinearity problem, while the heteroscedasticity test using the Glejser method stated that there was no problem. Based on the results of multiple linear regression analysis from the t test, it was found that partially product quality has a significant effect on purchasing decisions for Yamaha Mio motorcycle products, while promotion and design have a significant effect on the 5% level. From the results of the F test that simultaneously product quality, promotion and design have a significant effect on purchasing decisions for Yamaha Mio motorcycle products where the value of F count> F table. Product quality has the most dominant influence on purchasing decisions for Yamaha Mio motorcycle products. An R square value of 0.255 is obtained, which means that the dependent variable can be explained by an independent variable of 25.5% while the remaining 74.5% is explained by other variables outside the model.
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