A pricing model : key to Philippine motor vehicle assemblers' survival / 6
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Cecile, Santiago-Garcia.
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- xix, 299 pages 28 cm.
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Thesis (Ph.D.) -- Pamantasan ng Lungsod ng Maynila, 2004.;A dissertation presented to the faculty of the Graduate School of Management in partial fulfillment of the requirements for the degree Doctor of Business Administration.
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ABSTRACT: Building a local automotive industry can diversify and deepen the industrial structure of one country because of its vast linkages to many industries like metal, plastics, rubber, glass, paints and chemicals. Since the 1900s, the Philippine automotive industry has undergone four stages of development. The focus of this study is the fourth stage, the liberalization of the industry. The Local Motor Vehicle Assemblers have expressed negative reactions on neo liberalism for they believe that the industry is not yet ready for such orientation. Currently, a total of 18 Passenger Cars and Commercial Vehicle Development Programs of the Board of Investments. These assemblers are crying out for support to be able to survive the challenges that liberalization brings. And one of the steps they are seriously taking is the restructuring of their strategies on how to become competitive. It is within this context that this study is deemed necessary. This study could give an essential contribution to the Philippine Motor Vehicle Assemblers because it aims to develop a pricing model that could serve as an additional tool in restructuring the assemblers' marketing strategies to become price competitive. The study has come up with the following statement of the problem: What pricing model can be developed in order to help the Philippine Motor Vehicle Assemblers re-structure their pricing strategies? And its objective is to develop a pricing model that could be used as an additional tool in establishing new sets of strategies to support the objective of the Local Motor Vehicle Assemblers, to survive. A pricing model per vehicle category was built upon proving that the following hypotheses are false: 1) the predictor variables in the regression equation formed do not explain a large portion of variability in price; 2) The sample results used in forming the equation cannot be extrapolated to the population; 3) the equation formed does not explain a significant percent of the price variance; and 4) The derived equation does not reflect non-correlation of residuals from one time period to the next. In this study, both the qualitative and the quantitative tests were performed. In the qualitative test, a one-on-one interview with high profile officers from Toyota, Honda, Mitsubishi, Isuzu, Ford and CAMPI Secretariat was conducted to provide inputs on factors that they think affect the vehicle price. A purposive sampling was used in choosing the respondents. In the quantitative test, the population was stratified according to the Board of Investments' (BOI's) classification of vehicles. However, for Passenger Cars, the categories were further segmented according to the product position in the market. For Passenger Cars, the categories were: (1) Category I, (with engine displacement of 1.3L and below); (2) Category II, (with engine displacement of 1.4L to 1.5L; (3) Category III, (with engine displacement of 1.6L); and (4) Category IV, (with engine displacement of above 1.6L). While for Commercial Vehicles, the categories were: (1) Category I, Asian Utility Vehicles of up to three tons only and (2) Category II, Light Commercial Vehicles which includes Sports Utility Vehicles. A quantitative test was conducted to validate the results obtained from the qualitative test and also to check the above-mentioned hypotheses. To proceed with the test, a category representative was first selected among the various brand models belonging to a particular vehicle category. The brand model that consistently grasped the highest market share from 1998-2002 was chosen to represent all the vehicles of the same class. The one next to the highest was selected as the strongest competitor of the category representative. The next step taken was the gathering of data from 1998-2002 for independent and dependent variables. these data were used in performing the regression analysis. Information on the Suggested Retail Price, the dependent variable, of the chosen category representative, the Net Price of its strongest Competitor and the Promotion offered by the assembler were extracted from the Vehicle Assemblers' SRP report. The Vehicle Sales or the Demand for both the imported and the locally produced CBUs were taken from CAMPI Sales Report. The Excise Tax rate used was based on the Tariff Code published by the Philippine Tariff Commission from year 1998-2002. The Foreign Exchange Rate was provided by the Bangko Sentral ng Pilipinas (BSP) while the Interest Rate and the Inflation Rate were supplied by the National Economic Development Authority (NEDA). The Local Content Rate was based on the assemblers' reports. All the data gathered were run through a computer program called Eviews using the Ordinary Least Square Method to derive the needed regression equations. The statistical technique used in the formulation of equations was multiple regressions. It was applied for each vehicle category. For Passenger Cars, there were four categories and for Commercial Vehicles, there were two. Note that there were two sets of tests done for each vehicle category except for Category IV. The first set (A) included the Local Content data while the second set (B), the Local Content was not included. This was done in connection with the phasing out of Local Content Requirement in July 2003. Since there were two sets of tests per category, there were also two sets of pricing models built for some vehicle categories. For Categories IA, IIA, and IIIA of Passenger Cars and Category IA of Commercial Vehicles, a pricing model was built. For Category I-A of Passenger Cars, Toyota Corolla XL was chosen as the 1.3L and below category representative. The strongest competitor selected was Honda City. The null hypothesis that no equation can be formed in this category is not true because an equation derived The pricing model built is: Log (SRP)= 7.0498 + 0.227*Log (Forex)- 0.0140*Log(Promo) + 0.3920*Log(NPC)- 0.0243*Log (Supply) For Category I-B of Passenger Cars, Toyota Corolla XL was chosen as the 1.3L and below category representative. The strongest competitor selected was Honda City. The null hypothesis that no equation can be formed in this category is not true because an equation was derived. The pricing model built is: Log(SRP)= 7.0498 + 0.2227*Log(Forex)- 0.0140*Log(Promo) + 0.3920*Log(NPC) - 0.0243*Log(Supply) For Category II-A of Passenger Cars, Honda Civic Lxi MT was chosen as the 1.4L -1.5L category representative. The strongest competitor selected was Mitsubishi Lancer GLx MT. The null hypothesis that no equation can be formed in this category is not true because an equation was derived. The pricing model built is: Log (SRP) = 2.5205 - 0.0155*Log(Demand) + 0.0879*Log(LC) + 0.7648*Log(NPC) For Category II-B of Passenger Cars, Honda Civic Lxi MT was chosen as the 1.4L - 1.5L category representative. The strongest competitor selected was Mitsubishi Lancer GLx MT. The null hypothesis that no equation can be formed in this category is not true because an equation was derived. The pricing model built is: Log(SRP)= 4.2103 - 0.0158*Log(Demand) + 0.1966*Log(Forex) + 0.6441*Log(NPC) - 0.0098*Log(CBU) - 0.0277*Log(Int) For Category III-A of Passenger Cars, Honda Civic Vti-AT was chosen as the category representative. The strongest competitor selected was Toyota Corolla Gli AT. The null hypothesis that no equation can be formed in this category is not true because an equation was derived. The pricing model built is: Log (SRP) = 5.3138 + 0.0179*Log(CBU) + 0.2587*Log(Forex) - 0.0688*Log(Int) + 0.1117*Log(LC) + 0.5082*Log(NPC) For Category I-A of Commercial Vehicles, Toyota Revo GL was chosen as the category representative and the strongest competitor selected was Mitsubishi Adventure GLx. The null hypothesis that no equation can be formed in this category is not true because an equation was derived. The pricing model built is: Log (SRP) = 3.4761 - 0.0100*Log(Demand) + 0.1619*Log(Forex) + 1.4035*Log(LC) + 0.0113*Log(Promo) + 0.2393*Log(NPC) For Categories III-B and IVB of Passenger Cars and Categories IB, IIA and IIB of Commercial Vehicles, no pricing model was developed. For vehicles assemblers, it is recommended that the pricing model per category be used as one of the basis in formulating pricing strategies. This would make the price of the vehicle more reasonable and price competitive. It is also advised that the pricing model for each category should be constantly studies so that other significant factors that directly influence price changes should be incorporated. For the concerned government agencies, it is recommended that the pricing model be used prior to the formation and implementation of any new policies that would affect the price competitiveness of the vehicle assemblers. A careful study on product positioning and price determination is very important. And it is the belief of the researcher that the pricing models built would complement any pricing decision that one has to make for a more competitive pricing strategy.