PENGARUH PROMOSI TERHADAP KEPUTUSAN PENGGUNAAN OVO (Studi Pada Mahasiswa Fakultas Ekonomi dan Bisnis Islam IAIN Kediri Angkatan 2016)

Abstract

Experience in payments that are increasingly sophisticated in this era has become a trend in transacting when shopping for goods, paying a service or just paying monthly bills. The choice of payment method makes it easier for us to transact every day. One of them is electronic money. No exception with electronic money service from PT. Visionet International (OVO), they have a strategy in marketing their superior products  services including promotions, which is an activity in attracting consumers to use the company's goods or services through the advantages of a product goods or services offered. Where promotions conducted by electronic money service companies (OVO) have an influence on the decision to use their services. This research uses methods with a field research approach. with  descriptive quantitative analysis, simple regression analysis, coefficient of determination in interpreting existing phenomena. The results explain that promotion  and  usage  decisions  in  the  category  are  very  good.     Pearson's correlation analysis found a score of 0.661. This indicates that the promotional relationship with the purchase decision has a moderate regression relationship. From the results of the regression analysis obtained equation Y = 212.847 + 0.772X; if interpreted has the following meaning in the variable Decision Of Use (Y) will be worth 212,847 if there are no other variables that affect. If there is a Promotion  variable  (X)  intervention,  the  Usage  Decision  (Y)  variable  will increase by 0.772  basis  points  in  each  increment.  Based  on    the analysis  of determinant  coefficients,  the  value  of  R-square  =  0.4367  (43.67%),  which indicates  that the Usage Decision (Y) variable in the regression model is capable of being influenced by the Promotion variable (X) of 43.67%, and the remaining 53.33% is influenced by other factors outside the regression model.