Rusdi Dwi Iriansyah, Sugiono Sugiono


This research is motivated by the increased use of motorcycle sport 150 cc Yamaha in big cities in Indonesia, one of which is the use in the city of Semarang. Competition is extremely tight in the automotive industry especially two-wheeled vehicles require companies to continue to innovate in order to meet the tastes and needs of consumers who are continually changing. Competition is very competitive in the automotive industry motorcycle in Indonesia, one of the challenges to be faced by the automotive manufacturers motorcycle. Despite doing a variety of innovations to its products, not easy for automotive motorcycle manufacturers to make decisions appropriate marketing strategies to attract customers ,on the other hand the consumer today already begun critical of the goods / services to be selected by looking at several factors into consideration including product, price, promotion and distribution.

This analysis includes validity and reliability, classical assumption test, multiple regression analysis, hypothesis testing by T- test and F, and analysis of determination coefficien (R2). From the analysis above, we get a regression equation :

Y = 0,303 X1 + 0,269 X2 + 0,383 X3 + 0,194 X4

Based on the equation of multiple regression analysis the promotion variables have the most impact on purchasing decisions for 0.383, followed by products variable amounting to 0.303 and 0.269 for the price variable. While the distribution variables have an influence lowest compared to other variables for 0.194. Hypothesis testing using t tests showing that the four independent variables are product (X1), price (X2), promotion (X3) and distribution (X4) which investigated proved positively and significantly affect the dependent variable is the purchase decision. Then through the F test can be seen that the variables product, price, promotion and distribution eligible to test the dependent purchasing decisions. Adjusted R Square explained figures that 84.6% of purchase decisions variation can be explained by the four independent variables in multiple regression equations. While the rest of 15.4% is explained by other vaariabel beyond the four variables used in this study.


Purchasing Decision, Product, Price, Promotion, Distribution

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