Starbucks’s Stores and Canada’s Gross Domestic Product per Capita
The topic selected for this report is “Investigation of the relationship between the number of Starbucks’s stores per capita and Canada’s ‘GDP per Capita’. ” Starbucks Coffee Company (Starbucks) is a leading coffee brand that has the largest chain of stores. It is based in Seattle, Washington and its shares are registered on Nasdaq Global Select Market (Starbucks, 2017). It has 24, 000 stores in 70 markets globally. The company was established in 1971, and it is now the leading coffee retailing brand worldwide in terms of sales and number of stores that it operates in global markets. Its products include coffee, handcrafted beverages, merchandise, fresh food, tea, and ready-to-drink (RTD) (“Starbucks Company Profile” 2018).
The company started its operations in Canada in 1987, and it experienced similar growth over the years (“Our Canadian Story” 2018). Starbucks has more than 1, 400 company-operated and licensed locations across Canada. Although the company enjoyed phenomenal growth since its inception, the financial crisis during 2007-08 forced the company to close down thousands of its stores and revise its strategy to improve its product offerings and management of stores to avoid financial losses in its main U. S. marketplace. Similarly, the organization also closed straight down many stores within Canada in this year after 2012 because the economy slowed down down. It will be clear that companies’ performance is extremely affected by the particular economic growth associated with the nation in which usually they operate. Major Domestic Product (GDP), which is the entire value of goods and services produced and consumed by an economy, is an important indicator of economic growth (Brezina, 2012). On the basis of the company’s history, it is clear that its business and financial performance depends on the economic growth of countries in which it operates. As global economies revived in the last ten years or so, the company’s management successfully turnaround its business, and it reported consistent growth in that period.
Therefore, it would be interesting to investigate Starbucks’s business development measured by the number of stores that it opened in the last 13 years (2005-2017) in Canada. Furthermore, the estimation of correlation between Starbucks’s performance and ‘GDP per Capita’ of Canada is useful to forecast its future performance. A recent report by Trefis Team (2016) provides discusess the business strategy of Starbucks in the recent years. It indicates that the company’s strategy is to increase the number of stores operating in different markets. It wants to improve its market penetration and keep its operating costs down by opening drive-thrus or express stores in both urban and non-urban areas. It is important to investigate the relationship between business performance and GDP because these results can be generalized to other companies. Furthermore, it will also help in predicting changes in the business performance due to changes in economic conditions. It is important for a company’s managers to predict the vulnerability of its business to changes in macroeconomic factors such as economic growth that could create unsystematic or uncontrollable risks. The research question investigated in this report is given in the following.
Is there a correlation between the number of ‘Starbucks per Capita’ and ‘GDP per Capita’?
The research question set in this study is answered on the basis of the results of the correlation matrix and regression model that are implemented to determine the value of the coefficient between the two variables identified for this study. The dependent variable is ‘Starbucks per Capita, ’ and the independent variable is ‘GDP per Capita. ’ Both variables are measured on the basis of the total population of Canada. Furthermore, the regression model implemented in this study also considers “Year’ as another independent variable.
The report is structured in different sections that helps in investigating the relationship between the number of ‘Starbucks per Capita’ and ‘GDP per Capita’ of Canada. The first section of the report provides the topic of the current study and its main research question. Moreover, it gives the background of the study by highlighting its relevance and importance. The second section provides a comprehensive review of previous studies that also investigated the relationship between business performance and GDP. The third section provides a discussion of the theory that could be used to elaborate the relationship between the two variables. The fourth section provides findings of descriptive statistics, correlation matrix, and regression analysis along with their interpretation. Finally, the report gives conclusions regarding the relationship investigated in this research and also shows the possible places for even more research dependent on the restrictions from the current study.
There have been numerous studies which have looked into the business overall performance of companies internationally and factors influencing it. Most associated with these correctly regarded as macroeconomic factors influencing corporate performance. Given that the current study examines the partnership between business overall performance of Starbucks plus ‘GDP per Capita’ of Canada, consequently it of the particular report covers earlier studies which have furthermore performed an identical evaluation.
A research by Fidrmuc plus Korhonen (2010) looks at the impact associated with economic performance upon business cycles. It really is indicated that financial systems that face economic crisis tend in order to have a sluggish economic development which usually, in turn, impact the performance of companies. Therefore, there will be a significant effect of economic advancement on business period frequencies. Furthermore, the particular key literature simply by den Berg (2017) explains that financial development is the particular results of many aspects including population development, quantity of businesses, firms’ performance, and government policies, etc. The particular increase in financial activity creates fresh opportunities for people in order to earn higher earnings, which plays a role in the particular business development associated with companies as even more customers buy their own products and solutions.
On another hand, a research conducted by Schneider (2015) indicates that will the performance associated with a business will be significantly dependent upon the economic problems from the country within which it works. In this respect, it may be exemplified that will companies, which run in countries along with higher GDP like as China plus the United Says of America may generate more income (Leamer and Storper 2014). In this particular regard, it becomes evident that Canada’s strong ‘GDP per Capita’ significantly influence Starbucks’s business performance.
A study conducted by Marano et al. (2016) reveals the impact of a country’s GDP on business performance and corporate strategies. It concludes that high ‘GDP per Capita’ reflects that the economic condition of the country’s residents is stable and they have more money for spending on buying products and services. In this way, Starbucks is also affected by the economic growth of Canada as it has been reported that the company achieved larger market share over the years due to the rise in the country’s ‘GDP per Capita’.
A study by Wojcik (2016) supports the above statement by articulating that there are specific strategies of businesses that are influenced by the economic growth or high ‘GDP per Capita’ of the country. It is observed that retail firms have to increase the number of stores per capita in countries with strong ‘GDP per Capita’ (Wojcik 2016). This particular strategy of companies can be further understood by reviewing the study of Novales, Fernández, and Ruiz (2014) in which the researchers highlight that growing GDP of a country indicates that it has a vast pool of buyers. In this regard, companies operating in this country are also required to establish more retailing stores to fulfill the requirements of the market.
Furthermore, increasing the store per capita escalates the economic value and performance of businesses (Fraser 2013). A business, which usually has a big number of shops in any area, can generate even more profit per year when compared with other firms. Moreover, the availability of stores in various markets also suggests that the company has a higher market share and is capable of attracting a large number of customers (Fraser 2013). In this way, it could be articulated that there is a strong relationship between ‘GDP per Capita’ and business performance.
A study by Bjørnskov (2015) presents a contrasting overview regarding the number of store per capita in a country with high ‘GDP per Capita’. The study illustrates that functioning in a country with high ‘GDP per Capita’ can be complicated for businesses as it increases the burden on them to set more stores per capita and invest more on attracting customers as the market with higher GDP is relatively more competitive (Bjørnskov 2015). In such condition, it could be stated that the economic growth may have a negative impact on business strategies and performance.
A study by Issah (2017) examines the role of macroeconomic factors on the business performance of companies measured by financial indicators. The study included real GDP as one of the measures of firms’ performance and carried out a multiple regression analysis to find a negative relationship between return on assets and real GDP. It also shows that firms’ overall performance increase when the particular degree of economic exercise inside a country raises. Nevertheless , real GROSS DOMESTIC PRODUCT includes a significant, bad effect on it.
However, a research by Zhou plus Wu (2014) shows that the financial growth has better outcome on the particular business performance because it offers a system for the organization to earn even more profit by establishing up more shops per capita. This also improves the particular efficacy of the particular company to accommodate more customers simply by adopting technological development, which further improves the business overall performance (Zhou and Wu 2014).
The literature above shows that the financial growth has the positive impact upon business strategies and gratification. Thus, it can be tested within this study regardless of whether there is the strong correlation among Starbucks’s stores for each capita and ‘GDP per Capita’ associated with Canada delete word.
The discussion of earlier studies in this particular section forms the particular basis for setting up research hypotheses that will are tested through statistical testing of data. These are null and alternative hypotheses given below that are aimed at investigating the correlation between the number of Starbucks’s stores per capita and ‘GDP per Capita’ of Canada.
H0: There is no correlation between Starbucks’s stores per capita and ‘GDP per Capita’ of Canada.
H1: There is a positive correlation between Starbucks’s stores per capita and ‘GDP per Capita’ of Canada.
The relationship between business performance and economic growth could be explained by one of the models referred to as the Solow-Swan model that is set within the framework of neo-classical economic theory. The model elaborates that economic growth of a country depends on three factors including capital accumulation, availability of labor, and increased productivity (Novales, Fernández and Ruiz 2014). It could be used to explain the success of Starbucks in Canada as the company benefited from the growing number of immigrants coming to Canada that not only increased the size of the country’s population but also allowed the company to creates jobs. The company also accumulated earnings over the years that it used for expanding its network of stores. The New Growth Theory explains that the profit-generating motive of firms mainly leads to the economic growth of a country. It also states that technological advancement and adoption by businesses is crucial for economic development (McTaggart, Findlay and Parkin 2013). It is noted that Starbucks made significant changes to its strategy and integrated new information management systems to improve management of its stores and employees. Both model and theory suggest that there is a significant relationship between economic growth and business strategies aimed at improving the business performance.
The empirical analysis performed in this study is founded on the data associated with the two factors identified as ‘Starbucks per Capita’ plus ‘GDP per Capita’. The data for the first variable is collected from Statista, Worldometers, and World Bank’s websites. The amount of Starbucks’s shops situated in Canada through 2005 to 2017 was obtained through Statista (Statista 2018b). The number associated with stores was separated from the country’s populace to look for the values associated with the dependent adjustable ‘Starbucks per Capita’. The data from the country’s population has been obtained from the particular World Bank’s web site and Worldometers (Worldometers 2018, The entire world Lender 2018). Furthermore, the particular date of ‘GDP per Capita’ associated with Canada was gathered from Statista plus the World Bank’s website (Statista 2018a, The World Lender 2018). It can be stated that this data for this particular study is gathered from reliable web sites, which increased the particular internal validity associated with the research results of the evaluation part. The generalizability from the research results is restricted because the particular data utilized in the particular current study is just limited to the particular company’s operations within Canada. Nevertheless , this is suggested that will the same study approach could be used in another research to investigate the particular relationship between company performance and financial growth using a various dataset.
The detailed statistics calculated within this section are usually listed in Desk 1 along along with their values with regard to Starbucks and North america.
Table one: Descriptive Statistics.
In total, thirteen years’ data has been collected for each variables. The lead to value of ‘Starbucks per Capita’ will be 0. 0000321, which usually means that there are usually 32 stores associated with the company for each one million people. It may be stated that will this number will be small. However, the particular interpretation of this particular finding depends upon the geographical area of stores plus the distribution associated with population canada. These types of are important in order to consider before producing any claim concerning the company’s company position in North america. The information also shows that this number associated with stores of ‘Starbucks per Capita’ improved during 2005-2017. Nevertheless, a slowdown within this is seen in 2015 and 2016. The mean associated with ‘GDP per Capita’ is $457, thirty-two. 32, which means that Canada is 1 of the top economies in the particular world. Nevertheless , the particular country’s ‘GDP for each Capita’ also dropped in 2015 plus 2016.
It somewhat implies that the particular values of each variables changed within the same path. The low worth of standard mistake implies that the chosen sample sufficiently signifies the data populace that consists associated with the data associated with Starbucks and Canada’s ‘GDP per Capita’ since the firm started its procedures canada in 1987. The amount of Starbucks’s shops increased when the particular country’s ‘GDP for each Capita’ also improved. The median worth of both factors is close in order to the mean worth that means there was consistency in information. The standard change of both factors was low depending on their observed ideals. Furthermore, the skewness of both factors was negative, meaning that the histogram of those variables was skewed leftwards of the mean value. Moreover, the range of the selected variables was large because of the large difference in their minimum and maximum values. The minimum number of stores per capita was recorded within 2005, and the most was in 2014. However, the least expensive value of ‘GDP per Capita’ is at 2005, and the highest value has been recorded this year. 1 of the problems from the linear design implemented in this particular study may be the regular distribution of information. This is determined simply by creating histograms associated with the two variables’ values provided below.
Figure 1 displays that the regular distribution curve will be bell-shaped, which indicates that the situation of an ordinary distribution is met and the linear model is justified for using in this study.
Figure 2 also shows that the normal distribution curve of ‘GDP per Capita’ is bell-shaped, which means that the condition of a normal distribution is satisfied, and the linear model is justified for determining the relationship between the two variables selected for this study.
The coefficient of the correlation (r) between two variables is calculated by using the following formula.
Where n = number of observations, x = independent variable, and y = dependent variable.
The correlation matrix is developed in this study, and its results are provided in the following table.
Table 2: Correlation matrix.
Table 2 shows that the coefficient of correlation between ‘Starbucks per Capita’ and ‘GDP per Capita’ has a positive value of 0. 5945. The positive correlation between the two variables implies that the values of both variables change in the same direction. It means that if the country is experiencing growth and its ‘GDP per Capita’ is increasing, then businesses are also likely to report growth. The analysis of data shows that Starbucks continued to open new stores as the Canadian economy grew. However , the company closed down many stores due to the slowdown in the economy in 2009 and after 2012. The coefficient value is 0. 5945, which means that the relationship between ‘Starbucks per Capita’ and ‘GDP per Capita’ is moderate. It can be stated that 1% change in the country’s ‘GDP per Capita’ generates 0. 5945% change in the number of Starbucks’s stores in Canada. Table 2 also indicates the correlation between the two variables is significant at the 0. 05 degree. Additionally, it suggests that will there could become elements included within a study to check into the relationship among the quantity of shops per capita plus ‘GDP per Capita’ that would enhance the prediction of this particular relationship.
Scatterplot of ‘Starbucks per Capita’ and ‘GDP per Capita’
The scatterplot given in Figure 3 depicts that there is a positive relationship between ‘Starbucks per Capita’ and ‘GDP per Capita’ as the linear regression line is upward sloping. It also shows the linear equation that could be used to forecast the value of the dependent variable, i. e., ‘Starbucks per Capita’ by changing values of the independent variable, i. e., ‘GDP per Capita’.
The linear equation obtained from Figure 3 is given below.
y = 0. 00000000008x – 0. 0000006
Where R² = 0. 35338
The regression equation shows that the coefficient value is very small 0. 00000000008, and the value of R² is 0. 35338, which means that this linear model just explains 35. 338% of the complete variations in the particular selected dataset associated with the number associated with Starbucks’s stores for each capita in North america. The next stage from the analysis requires to enhance the predictive capability of the particular model, which will be explained in the particular next sub-section of the report.
A geradlinig regression model will be implemented in this particular study to figure out the relationship between dependent variable, we. e., ‘Starbucks for each Capita’ and the particular independent variable, we. e., ‘GDP for each Capita’. In this particular model, an extra adjustable, i. e., ‘Year’ is also additional. The reason with regard to this is it would improve the particular outcome of the regression analysis in terms of better prediction of the value of the coefficient of the relationship between the two key variables. The results of the multiple regression model are provided in the following table.
Table 3: Summary Output.
It could be noted from Table 3 that the value of R² has improved significantly from 0. 35338 to 0. 9399. It means that this regression model has a better capability of predicting the relationship between dependent and independent variables included in the model. The model explains almost 94% of the total variations observed in 13 data entries. Moreover, Table 3 indicates that the value of the standard error is very low that suggests that the outcomes of the model implemented in this study suitably represents the relationship between the two variables.
Table 4: ANOVA.
Table 4 indicates that the total variations in the dataset were small. Moreover, the regression model explains the majority of the variations recorded during these values. The worth of significance Farrenheit is less compared to the conventional error phrase of 5% because the level of confidence thought for this research is 95%. This implies that the particular results from the regression model are dependable and associated with the particular relationship investigated with this report.
Table 5: Coefficients.
Table 5 will be generated from the particular regression model applied with this study. This indicates that this pourcentage of the continuous, β0 is -0. 003144574, which signifies the sum associated with residuals not described by the design. The first pourcentage from the slope, β1 between ‘Starbucks for each Capita’ and ‘GDP per Capita’ will be 3. 49826E-10. Given that the coefficient worth is positive, consequently it could become indicated that the particular relationship between these types of two variables is positive. It means that when the value of the independent variable increases, the value of the dependent variables also increase. The second coefficient of the slope, β2 between ‘Starbucks per Capita’ and Year is also positive. Nevertheless , its benefit is greater compared to the first pourcentage, β1, which indicates that there exists a stronger relationship between ‘Starbucks per Capita’ plus Year than ‘Starbucks per Capita’ plus ‘GDP per Capita’.
This obtaining is maintained the particular analysis of p-value of both associations. It is mentioned that the p-value of ‘GDP for each Capita’ is zero. 0152, which will be more than the particular standard error phrase of 5%. This means that this partnership between ‘Starbucks for each Capita’ and ‘GDP per Capita’ will be found to become insignificant. It really is not significant even if the confidence level of 90% is considered. It is inconsistent with the finding of the correlation matrix as the correlation between the two variables is found to be significant without the variable of ‘Year. ’ On the other hand, the p-value of ‘GDP per Capita’ is 1. 77262E-06, which is less than the standard error term of 5%. It means that the relationship between ‘Starbucks per Capita’ and Year is found to be significant. These results are discussed in the following to provide results regarding the validation of the hypotheses set out in this study for testing the relationship between the two variables. Based on the findings of the regression model, the following linear equation is formed to estimate the impact of explanatory variables on the dependent variable.
‘Starbucks per Capita’ = -0. 003144574 + 3. 49826E-10 x ‘GDP per Capita’ + 1. 57167E-06 x Year
This equation can be used to forecast the number of Starbucks stores in the coming years depending on the estimation of ‘GDP per Capita’. The forecasted value of ‘Starbucks per Capita’ is determined to be 0. 00004318 in 2018 based on the average growth of ‘GDP per Capita’ ($46, 094. 7). The residuals of the regression model are provided in the following for the last 13 years.
Table 6: Coefficients.
The sum and mean value of residuals is nil, which also satisfies the assumption of the regression model.
Discussion of Findings
The findings of the current study are discussed in a way that a comprehensive answer to the research question stating “Is there a correlation between the number of ‘Starbucks per Capita’ and ‘GDP per Capita’? ” is provided for better understanding of the research approach adopted in this study and the results obtained from statistical testing of the data collected for the identified variables in this report. The current study aims to investigate the relationship between the number of Starbucks’s stores per capita, which is calculated as the number of Starbucks divided by the total population of Canada, and ‘GDP per Capita’. The study draws its methodology from previous studies that also conducted a similar study of the relationship between the two variables selected for the current study. The trend analysis of the values of the two variables indicates Starbucks underwent major financial problems because of the financial crisis during 2007-2008. The company’s management decided to close down a large number of its stores globally.
A similar trend was noted in 2009 as the number of Starbucks’s stores in Canada declined because the management decided to close down the company’s stores to reduce its operating losses. The strategy change followed by its strict adherence allowed the company to turnaround its position in the coffee market. It can be noted that will the number associated with Starbucks continued in order to increase till 2014 as it once again faced challenges because of the slowdown in Canada’s economic progress. Consequently , it is obvious there exists the relationship between the particular historical regarding Starbucks in Canada plus GDP. It can become explained simply by the neo-classical theory of financial growth, which means that Starbucks invested within expanding its string of stores all through Canada as the particular economy grew. Whenever Canada’s economy enhances, businesses like Starbucks take advantage associated with the increasing need for their items and services. These people are also capable to manage their procedures in a much better way by managing their costs or even distributing them more than a larger quantity of sales areas. It is obvious that Starbucks’ administration was efficient in order to take full benefit of the possibilities that the developing economy of North america provided to it.
The statistical screening from the data, we. e., correlation matrix confirmed that presently there is a good relationship between the dependent variable, ‘Starbucks per Capita’ and the independent variable, i. e., ‘GDP per Capita’. The value of correlation coefficient implied a reasonable relationship between the two variables, which means that several other factors could have been included in the analysis to predict better changes in the number of stores operated by Starbucks in Canada. The correlation matrix also shows that the correlation between the two variables is significant at the confidence level of 95%. The positive correlation is consistent with the findings of previous studies including Zhou and Wu (2014), Frase (2013), and Novales, Fernández, and Ruiz (2014) as they also found businesses to experience growth when there is an improvement in the economic development of the country in which they operate. However, this finding is different from those presented in the study by Bjørnskov (2015).
The key part of the analysis performed in this report comprises of the results drawn from the multiple regression model, which also included another variable representing time. It improved the goodness of fit as the value of R-squared substantially improved from the value of 0. 35338 found in the previous linear model. The coefficient of the slope between ‘Starbucks per Capita’ and ‘GDP per Capita’ is positive, which also confirms the results of the correlation matrix. However, this model does not find this relationship to be significant, which contradicts the results of the correlation matrix. The findings of the regression analysis are consistent with previous studies including Marano et al. (2016) and Leamer and Storper (2014) that also used statistical analysis to determine the relationship between business performance and economic growth.
On the basis of the findings of both correlation matrix and regression analysis, the answer to the research question set out in the current study is the fact that there is a positive correlation between the number of stores of ‘Starbucks per Capita’ and ‘GDP per Capita’ of Canada. The results of the present study are consistent with all those of previous research that are talked about in the books review section associated with this report. This implies that the outcomes of this research are reliable plus relevant. Starbucks will be the largest espresso retailer globally plus its business technique to increase the quantity of stores within different markets comes from the scale associated with its operations that will it wants in order to achieve in developing economies across the particular world. Starbucks is a great example of companies that use the number of their physical stores as an indicator of their growth. They attract new customers through their physical stores in countries with rising GDP per capita.
Both correlation matrix and regression analysis indicate that the coefficient of relationship between ‘Starbucks per Capita’ and ‘GDP per Capita’ is positive. However, it is not found to be significant based on the comparison of the p-value. Therefore, the null hypothesis, which states that there is no significant positive relationship ‘Starbucks per Capita’ and ‘GDP per Capita’ is accepted. On the other hand, the alternative hypothesis, which states that there is a significant positive relationship ‘Starbucks per Capita’ and ‘GDP per Capita’ is rejected. Although the objective of the current study is not to investigate the relationship between ‘Starbucks per Capita’ and ‘Year’, it is also examined in this research to enhance the outcomes of the regression model. It will be found that the particular coefficient of partnership between ‘Starbucks for each Capita’ and ‘Year’ is positive. Furthermore, it is discovered to be substantial in line with the comparison associated with the p-value. Consequently, the null speculation, which states that will there is simply no significant positive partnership ‘Starbucks per Capita’ and ‘Year’ will be rejected. Furthermore, the particular alternative hypothesis, which usually states there is the significant positive partnership ‘Starbucks per Capita’ and ‘Year’ will be accepted.
The study investigates the relationship among the business overall performance of Starbucks plus economic regarding North america. The study applied in this statement is quantitative, plus statistical tests carried out in this study include calculation of descriptive statistics, correlation matrix, and regression analysis. The study adopts the deductive approach by providing a theoretical analysis of economic growth and its impact on business performance and critical discussion of previous studies that have also investigated the relationship between the two variables including ‘Starbucks per Capita’l (dependent variable) and ‘GDP per Capita’ (independent variable). The two hypotheses are established that are put into testing on the basis of the statistical tests performed in this study.
Both correlation matrix and regression analysis generated a positive coefficient of the relationship between ‘Starbucks per Capita’l and ‘GDP per Capita’. However, there are conflicting results related to the significance of this relationship as the correlation matrix indicates that there is a significant relationship between ‘Starbucks per Capita’l and ‘GDP per Capita’. On the other hand, the regression analysis, which added another variable representing time in the analysis, shows that there will be an insignificant among ‘Starbucks per Capita’l and ‘GDP for each Capita’. The affirmation of hypotheses will be carried out based on the results associated with the regression design, and it will be figured the null hypothesis is declined as well as the alternative speculation, which states that will there is an optimistic relationship between ‘Starbucks per Capita’l plus ‘GDP per Capita’ is accepted.
Limitations of Present Research and Recommendations
There are several limitations of the particular current study plus methodology adopted in order to carry out the particular analysis. The 1st limitation is the particular limited sample dimension, which could happen to be extended by gathering data of Starbucks and Canada’s economic growth from the time the company started its business in Canada. The second limitation of the methodology is it somewhat lacks descriptive (qualitative) aspect of research. It could have been enhanced by performing informational analysis of the company’s reports to highlight and discuss factors that affected its business in Canada and also review strategic changes made by the management to deal with the challenges faced by the company in the last 13 years. The 3rd limitation is that will there is absolutely no comparative evaluation performed in this particular report, meaning that presently there is low generalizability of results from statistical testing of information.
This restriction could be conquer inside a future research by including some other companies within the test, for example, McDonald’s or KFC which are leading international meals companies operating within Canada. There are usually other microeconomic plus macroeconomic factors like governmental policies, pumpiing, FDI, and customer spending, etc. that will also affect the particular performance of businesses within a nation. Therefore, any long term study could consist of these factors within the model to check into the relationship among business performance plus economic growth. Lastly, it requires to become highlighted that this partnership between business overall performance and economic development is two-way, which usually means that financial growth creates possibilities for your business to create that eventually adds to the extra economic development within the future. Consequently , the same evaluation might be done simply by considering ‘GDP for each Capita’ since the reliant variable as well as the ‘Starbucks per Capita’ because the particular independent variable.
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