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Domino’s Pizza’s Entry into the Estonian Market Research Paper

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Updated: Jul 17th, 2020

About Domino’s Pizza

Domino’s Pizza is a multinational public company trading in the New York Stock exchange, and existing in over 70 countries all over the world. Founded in the 1960s, Domino’s Pizza currently owns over 10, 000 outlets with over 140,000 employees. The company carries out restaurant services. It sells a variety of foodstuffs such as pizza, pasta, boneless chicken, wings, chicken & bacon carbonara, spinach and feta, Italian sausage & pepper trio, and Tuscan salami & roasted veggie among others. The company is located in over eight countries such as Brazil, South Korea, Netherlands, Belgium, New Zealand, and France, among others. Domino’s Pizza would like to make an entry into Estonia Market. This paper is carrying out a demographic survey of Estonia Market. It collects demand data for industry demand and makes forecasts for Domino’s Pizza. Finally, it gives recommendations to the management of the company on whether to venture into that market or not.

Demographics of Estonia

Estonia occupies a total area of 45,226 square kilometers. The country is located in Eastern Europe, bordering on Russia and Latvia. The country’s total population was 1,332, 893 in 2005. The population growth rate was -0.65% in the year 2005. The table below shows additional details on the demographic of the country.

Variable Data
Age Structure 0-14 years: 15.5% (male 106,300/female 100,446)
15-64 years: 67.7% (male 429,843/female 472,034)
65 years and over: 16.8% (male 74,037/female 150,233)
Median Age Total: 39.06 years
male: 35.52 years
female: 42.35 years (2005 est.)

Economy of Estonia

The country’s economy relies on the service sector, followed by production and agriculture. Machinery and equipment make a larger proportion of imports and exports. The GDP of the country for the past five years has been fairly stable. The table below shows GPD of the country for the past five years.

2007 2008 2009 2010 2011
GDP in USD current prices 21,993,658,963 23,882,826,894 19,225,808,565 18,822,715,730 22,184,722,472

Source of data – The World Bank Group, 2012

From the table, it is clear that the GDP of Estonia over the five years is stable. GDP increased from $18 billion in 2010 to $22 billion in 2011. This shows that the country is a viable investment destination. Besides, as mentioned above, the service industry tends to thrive more in the country than in other sectors. Finally, the government legislations are favorable and make it possible to carry out business with ease in the country.

Independent and dependent variables

Before starting operations in a new market, it is necessary to carry out a comprehensive feasibility study in all aspects of a business. In this paper, the emphasis is put on demand forecasts for the company. The demand curve shows the relationship between the quantity demanded and the price of goods (or services). There are several determinants of demand. These determinants cause either a movement or a shift of the demand curve. Besides, these factors can either impact negatively or positively the demand. The price of the good is a key determinant of demand. Increase in price of their own good causes a decline in demand. The price of related goods also affects demand. Goods can either be substitutes and complements. Increase in price of substitutes leads to an increase in demand of the good while a decrease in price of complement leads to a decline of quantity demanded. The number of buyers also impacts the demand of the good. Increase in buyers leads to increase in demand. Customers’ future expectations affect current demand. Customers will reduce their current demand when they expect favorable future prices. Advertisement also affects demand positively. Regression analysis will be carried out to estimate the demand equation. The regression uses one dependent variable and six independent variables. The depended variable is the quantity demanded while the independent variables are price, advertisement expenditure, income, price of complementary goods, future price changes, and price of substitute. These variables lead to multivariate regression.

Demand equation

The regression line will take the form Y = a0 + a1X1 + a2X2 + a3X3 + a4X4 + a5X5 + a6X6. The theoretical expectations are a1 can take any value, a2 >0, a3 > 0, a4 < 0, a5 < 0, and a6 > 0. The result of regression for each independent variable is shown in the table below.

Variable Coefficients of the variable
a0 Intercept 133.9098744
X1 Price -13.97999913
X2 Advertisement 9.457390346
X3 Income 0.122133164
X4 Price of complementary good -0.376948039
X5 Future price changes -0.899879156
X6 Price of substitute 1.610558603

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

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

Coefficient of determination

Coefficient of determinations shows the proportion of variation of the dependent variable explained by the independent variables. A high coefficient of determination implies that the explanatory variables explain variations. A low value of coefficient of determination implies that the explanatory variables do not explain the variations in demand adequately. For this regression, the value of coefficient of determination is 98.33%. This implies that the independent variables explain 96.32% of the variation in demand meaning that the explanatory variables strongly determine the demand function.

To improve on the value of the coefficient of determination, variables which are not statically significant can be dropped. Alternatively, more variables can be included in the formulation. For instance, in this case consumers taste and preferences can be included in the equation since it is a strong determinant of demand. This may improve the value of coefficient of determination.

Testing statistical significance of the variables

Testing statistical significance shows whether each and every variable is a significant determinant of demand. Since the sample size is small that is less than thirty, t-test is used to test the significance of the variables. A two tailed test is carried out at 90% level of confidence.

Null hypothesis: Ho: ai = 0

Alternative hypothesis: Ho: ai ≠ 0

The table below summarizes the results of hypothesis testing.

Variable t – values T at α 0.05 Decision
a0 Intercept 5.554431 1.9432 Reject
X1 Price -9.87419 1.9432 Reject
X2 Advertisement 2.87356 1.9432 Reject
X3 Income 0.113082 1.9432 Do not Reject
X4 Price of complementary good -0.49849 1.9432 Do not reject
X5 Future price changes -0.668 1.9432 Do not reject
X6 Price of substitute 1.043683 1.9432 Do not reject

The null hypothesis implies that the coefficients are not significant determinants of demand. The alternative hypothesis implies that significant is significant determinant 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 which are 10% of level of significance. The other four variables are income, price of complementary goods, future price changes, and price of substitute are not statistically significant at 10% confidence interval.

The demand equation formulated is not strong enough to predict future values because out of the six variables only two are statistically significant determinants of demand. It is necessary to include more variables into the analysis such as age, taste and preferences. Alternatively, the sample size can be increased collected at different times. This helps in improving the value of estimates.

Forecasts for the next four months

The table below summarizes the results of forecast over during the four months.

Qty Price Advertising Expenditures Income Price of complementary Future price changes Price of substitute
99.95 7 6.7 5.5 5.92 5.41 4.33
100.28 7.02 6.77 5.51 5.93 5.43 4.34
104.12 7.04 7.21 5.53 5.95 5.45 4.36
104.89 7.07 7.32 5.55 5.97 5.47 4.37

From the above table, it is clear that demand will grow over the four months period. That is 99.95 units in the first month, 100.28 in the second month, 104.12 in the third month, and 104.89 in the fourth month. This is growth of about 0.333% per month. The forecast are made on the assumption that all the explanatory variables will grow. The growth rate is assumed to be 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, this analysis only looked at the economic aspect of the venture. It is important to carry out comprehensive feasibility study of all aspects of the business.

References

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

US Census Bureau, (2012). . Web.

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IvyPanda. (2020, July 17). Domino’s Pizza’s Entry into the Estonian Market. Retrieved from https://ivypanda.com/essays/dominos-pizzas-entry-into-the-estonian-market/

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"Domino’s Pizza’s Entry into the Estonian Market." IvyPanda, 17 July 2020, ivypanda.com/essays/dominos-pizzas-entry-into-the-estonian-market/.

1. IvyPanda. "Domino’s Pizza’s Entry into the Estonian Market." July 17, 2020. https://ivypanda.com/essays/dominos-pizzas-entry-into-the-estonian-market/.


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IvyPanda. "Domino’s Pizza’s Entry into the Estonian Market." July 17, 2020. https://ivypanda.com/essays/dominos-pizzas-entry-into-the-estonian-market/.

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IvyPanda. 2020. "Domino’s Pizza’s Entry into the Estonian Market." July 17, 2020. https://ivypanda.com/essays/dominos-pizzas-entry-into-the-estonian-market/.

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IvyPanda. (2020) 'Domino’s Pizza’s Entry into the Estonian Market'. 17 July.

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