ANALISIS FAKTOR INTANGIBLE PADA HARGA PRODUK (ANALYSIS OF INTANGIBLE FACTORS TO PRODUCT PRICING) - , Reza Bayu Kurniawan ([Yogyakarta] : Universitas Gadjah Mada, 2014)
Pricing is a very important thing for the company. Price is one aspect which directly determines companyâs revenue from products sold, but it also shows the companyâs strategy. Thus, the decision to determine price is very vital for the company. In determining price, company does not only consider measurable factors (tangible), but it must also consider non-measurable factors (intangible). Focus on intangible factors will increase the companyâs value affecting pricing decision. The importance of measuring intangible value is the background in this study. The research aims to develop a prediction model of intangible value for pricing. Objects used in this study are 20 companies in special region of Yogyakarta covering shopping-specialty product category. Intangible value is predicted from intangible factors considered. These factors are brand, innovation, length of relationship, and perceived value. The value of brand, length of relationship, and perceived value will be measured on how consumer get the understanding of these factors on the object. Innovation is obtained from company portfolio, while intangible value is obtained from company financial data. To develop a predictive model of intangible value will be used 2 methods namely multiple regression analysis and model of Kano. Intangible value as the dependent variable, while brand, innovation, length of relationship, and perceived value as the independent variable. There is a relationship between the value of the intangible and product category. Prediction model of intangible value (y) as a function of product functionality (x), where the functionality equation is constructed using 4 intangible factors. Prediction model of intangible value has R2 value of 70,2%. Product functionality equation is built using 2 methods namely multiple regression analysis and model of Kano. The selected equation (x) is the model developed using multiple regression analysis with R2 value of 57,5% and the value of predictive ability of 90.3%.