Domino’s Pizza: Malaysian Market Demand Forecast Research Paper

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Objectives of the paper

This paper attempts to carry out a demographic survey of Malaysia Market. It collects demand data and makes forecasts for Domino’s Pizza. Finally, it gives recommendations to the management of the company on whether to venture in to that market.

Demographics of Malaysia

The country’s total population was 23, 953,136 in 2005. The population growth rate was 1.8% in the 2005 census. The country’s per capita income is $9,700.

Economy of Malaysia

The country’s economy relies on service sector. Industrial productions make a larger proportion of both imports and exports. The table below shows the country’s GDP over the past five years.

20072008200920102011
GDP in USD current prices186,777,261,970.74222,744,224,712.38192,911,631,102.08237,796,914,597.18278,671,114,816.94

Source of data – The World Bank Group, 2012

Independent and dependent variables

Demand curve depicts the inverse relationship between the quantity demanded and the price of goods (or services). There are several determinants of demand. Price of the good is a significant determinant of demand. Increase in price of the commodity causes a decline in demand. Price of related goods, either substitutes or complements, affects demand.

Increase in price of a substitute commodity leads to an increase in demand while a decrease in price of complement leads to a decline of quantity demanded. Secondly an increase in buyers leads to increase in demand. Customers’ future expectations also affect current demand. Customers reduce their current demand when they expect favorable future prices.

Advertisement affects demand positively. Regression analysis estimates the demand equation. The regression uses one dependent variable and six independent variables (McGuigan, Moyer, & Harris, 2008). The depended variable is the quantity demanded. The independent variables are price, advertisement, income, price of soda, future price changes, and price of pasta.

Demand equation

The regression line takes the form Y = b0 + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + b6X6. The theoretical expectations are a1 takes any value, b2 >0, b3 > 0, b4 < 0, b5 < 0, and b6 > 0.

Results of regression

VariableCoefficients of the variable
b0Intercept133.9098744
X1Price-13.97999913
X2Advertisement9.457390346
X3Income0.122133164
X4Price of soda-0.376948039
X5Future price changes-0.899879156
X6Price of pasta1.610558603

Source of data for analysis of demand – US Census Bureau, 2012

From the above table, the regression equation takes the form Y = 133.91 – 13.98X1 + 9.46X2 + 0.12X3 – 0.38X4 – 0.90X5 + 1.61X6.

Coefficient of determination

Coefficient of determinations shows the amount of variation of the dependent variable that the independent variables explain. For this regression, the value of coefficient of determination is 98.35%. This implies that the independent variables explain 98.35% of the variations in demand.

To improve on the value of the coefficient of determination, variables which are not statically significant need to be dropped. Alternatively, more variables can be included in the formulation.

Testing statistical significance of the variables

Testing statistical significance shows whether each variable is a significant determinant of demand. Testing the statistical significance of the variables makes use of t-test. This is because the sample size is small. Hypothesis testing uses a two tailed test at 90% level of confidence.

Null hypothesis: Ho: ai = 0

Alternative hypothesis: Ho: ai ≠ 0

Results of hypothesis testing

Variablet – valuesT at α 0.05Decision
b0Intercept5.5544311.9432Reject
X1Price-9.874191.9432Reject
X2Advertisement2.873561.9432Reject
X3Income0.1130821.9432Do not Reject
X4Price of complementary good-0.498491.9432Do not reject
X5Future price changes-0.6681.9432Do not reject
X6Price of substitute1.0436831.9432Do not reject

The null hypothesis implies that the coefficients are not significant determinants of demand. Rejecting null hypothesis implies that the variables are statically significant. From the table above, price and advertisement are the only statistically significant variables are 10% level of significance. The other four variables are not statistically significant at 10% confidence interval.

The demand equation formulated is not strong enough to predict future values. This is because only two are statistically significant. Addition of variables during formulation makes the estimated demand equation accurate for forecasting. In addition, the sample size should be increased.

Forecasts for the next four months

The table below summarizes the results of forecast for four months.

QtyPriceAdvertising ExpendituresIncomePrice of complementaryFuture price changesPrice of substitute
112.107.048.045.535.955.444.356
149.587.0812.075.565.985.474.37
206.087.1218.105.5836.015.504.39
291.137.1627.155.616.045.324.42

From the above table, demand grows over the four months period. The forecast assumes that advertisement grows at 20% while the remaining explanatory variables grow at 0.5833% per period. The growth rate takes the real rate of GDP growth rate in the country.

Recommendations

Management of Domino’s Pizza Company should go ahead and invest in the country. However, final decision should be made after carrying out comprehensive feasibility study of all aspects of the venture.

References

McGuigan, J. R., Moyer, R. C., & Harris, F. H. D. (2008). Managerial economics: Applications, strategy, and tactics. Mason, OH: South-Western Cengage Learning.

The World Bank Group, (2012). Data. Web.

US Census Bureau, (2012). Data access tools. Web.

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IvyPanda. (2019, June 14). Domino's Pizza: Malaysian Market Demand Forecast. https://ivypanda.com/essays/forecasting-of-demand/

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"Domino's Pizza: Malaysian Market Demand Forecast." IvyPanda, 14 June 2019, ivypanda.com/essays/forecasting-of-demand/.

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IvyPanda. (2019) 'Domino's Pizza: Malaysian Market Demand Forecast'. 14 June.

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IvyPanda. 2019. "Domino's Pizza: Malaysian Market Demand Forecast." June 14, 2019. https://ivypanda.com/essays/forecasting-of-demand/.

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