Analysis of Emerging Markets for Franchising Tropical Smoothie Café
Introduction
Tropical Smoothie Café is a USA-based restaurant company that offers various made-to-order smoothies, pies, and sandwiches. The company began in 1997 as a beach shop in Florida, offering smoothies alone (TFC). Over the years, Tropical Smoothie Café has continued to grow its menu and open new cafés across the country. By 2018, the company had over seven hundred Cafés across the USA. The company has shown a huge appetite for expanding its territory. Today, the company has more than eight hundred shops in the USA. However, the company is yet to expand to territories outside the USA. International expansion could potentially grow the company’s resources by great margins. The USA territory represents only about four percent of the world’s population. One the other hand, the majority of the world populace is part of emerging markets (Alon). Consequently, the company must carry out a proper analysis of various potential suitable markets to inform its strategies for successful entry into international markets. Therefore, this study will evaluate and compare emerging markets in China, Taiwan, Korea, and Hong Kong to determine the most suitable market for franchising Tropical Smoothie café.
There are two existing frameworks for market analysis, which most market analysts employ in appraising potential franchising markets. One of those frameworks is the total urban market potential framework adjusted to spending in the specific industry of analysis, such as the service industry (Alon). Alon’s framework provides an empirical method for evaluating the industry-specific size of potential markets for a company in various emerging markets. The second framework is the composite score index framework developed by Aliouche and Schlentrich. The composite score index framework provides a methodology for comparing various factors affecting franchising decision making in different countries. This study will use both of these methodologies to analyze and recommend the most suitable emerging market among China, Taiwan, Hong Kong, and Korea for TSC, which is a quick-service restaurant.
The paper will include four sections. The first section is the introduction, which has given introductory information on Tropical Smoothie café and the frameworks for analyzing its franchising into Asian markets. The second section will provide in-depth information on the approaches of the two frameworks. The third section will use the two frameworks to analyze market attractiveness among China, Taiwan, Hong Kong, and Korea markets. The fourth section will develop recommendations for Tropical Smoothie café’s future franchise expansion based on both frameworks. Additionally, the fourth section will evaluate the shortcomings of both frameworks. Also, it will suggest measures to improve the two frameworks for better market analysis in the future.
The Approaches of the Two Frameworks
Adjusted total urban market’s potential framework
The framework assesses the economic potential of markets by incorporating four variables, which Alon describes as the key variables for describing economic potential. These four variables are GPD per capita, population, income distribution, and urbanization. Alon evaluates how each of these variables can describe the potential of a region. First, the level of inhabitants in a country may be useful when evaluating the potential market size. Alon notes that greater populations do not necessarily result in greater market sizes because, in certain populations, such as India, almost ninety percent of the population is living below two dollars per day.
The second variable is the GDP per capita, which may be useful when comparing the purchasing power of different groups of markets. According to Alon, most emerging markets have either a lower middle income or upper-middle-income rating under the annual World Bank records. However, the author argues that this variable may mislead the analyst because it does not take into account the variations in the cost of inputs and living between the compared economies. The third variable that previous models used to analyze economic regions was the level of urbanization. According to Alon, more than seventy percent of franchise systems consider urbanization as a crucial factor in franchising decisions. However, the author argues that urbanization alone is not a sufficient variable for appraising emerging markets.
The last variable that Alon considers important in appraising emerging markets is income distribution. According to the researcher, GDP per capita only indicates an average of a country’s purchasing power. He argues that the distribution of incomes among the populace may be a factor for franchising success. Economies with disproportionate income distribution may give a false impression that they have a better purchasing power. In certain economies, there are very few groups earning a disproportionately higher income than the majority at the bottom of the economy. In such economies, understanding the income distribution of the countries may help in making better franchising decisions.
Alon’s proposed framework combines the four variables to develop an accurate depiction of the purchasing capabilities and the market size of compared emerging markets. First, he adjusts the population data of the appraising markets into an adjusted purchasing power parity (PPP) GDP, which is an index to indicate the purchasing power of the country’s citizens. Alon obtains the PPP GDP by multiplying the population data with the GDP per capita of each country. The second step in the framework is to calculate the per capita income of each market that is adjusted to savings. The researcher argues that people can only spend the money they have as savings. Therefore, calculating the GDP per capita of savings can be a better indicator of the purchasing power of the market. Alon calculates this variable by multiplying the GDP per capita of each country with the percentages of domestic savings recorded by the World Bank.
The next step in the methodology is to narrow down the income of the people that they spent on the specific industry, which the analyst is seeking to franchise. In the case of Tropical Smoothie Café, the industry is the service industry. Alon multiples the percentage of GDP income spent on the service industry by the GPD per capita savings to obtain the income spent on service products. Finally, the framework calculates the potential of urban markets for the franchising industry in each country by multiplying the adjusted spending on service products with the urban population of each country. The adjusted total urban market potential for the service industry indicates the size of markets that directly relates to the franchising company.
The composite score index framework
The composite score index framework is an alternative method for evaluating the possibility of achieving success for a franchising company. Aliouche and Schlentrich developed this method by reviewing three previous models of internationalization. The three models are the eclectic paradigm, the Uppsala model, and the transaction cost analysis approach. According to the Uppsala model, franchising companies prefer countries that are closer to theirs both geographically and psychically. On the other end, the eclectic model argues that franchising companies base their decision to internationalize on the achievability of three crucial advantages. These advantages are ownership advantages, internationalization advantages, and location advantages. Finally, the transactional cost model argues that franchising is a trading-off process between the benefits of integration and the cost of integration. The researchers have combined the elements of these three models to develop a better model for franchising decision making.
The method aims to arrange a set of emerging markets according to the potential of gaining more profit and maximizing shareholder value. The method focuses on the costs of integration and operation in the emerging markets. It uses firm-specific, country-specific, and industry-specific factors to evaluate the suitability of franchising into those markets. Also, the framework utilizes ranking tables of countries’ performances in those different factors to develop an average performance ranking table for each market on the various factors of interest. Finally, the model develops a composite index ranking of the suitability of each emerging market to the franchising firm based on a ratio of the importance of the average performance ratings. The final composite index-ranking table indicates the optimum market-entry mode that has the possibility of achieving maximum franchising success for the firm.
The framework focuses on four factors that have the greatest impact on franchising success. These four factors are the cultural-geographic distance between parent and foreign markets, market opportunity, political-economic risks, and legal-regulatory risks. According to Aliouche and Schlentrich, these four factors encompass the opportunities and risks of internationalization for any USA company. Additionally, Aliouche and Schlentrich surveyed management members of previously franchised firms. The survey aimed to ask the respondents to compare the importance of opportunities and risks in making franchising decisions. The researchers used the results of the survey to the final indexing formulae for the various ranking tables of averaged market performances.
First, the authors used the World Bank’s annual ranking of countries based on population and GDP per capita. The researchers computed the average of each country’s ranking-index from the two tables to obtaining a market opportunity index-ranking table. The second step involves generating a composite country risk index for the group of emerging markets under evaluation. The researchers elected to employ Euromoney’s Country Risk Index, which has a biannual update. The risk index indicates a weighted average of different risk dimensions for various countries, such as political stability, debt indicators, access to capital markets, etc.
The next step is identifying a ranking table that captures the legal and regulatory risks of each emerging market. The researchers suggest that the “Ease-of-Doing-Business” index report by the World Bank is a good indicator of legal and regulatory risks. The annual report ranks all the countries of the world in the order of the ease of setting up companies based on ten factors. These factors include getting credit, licenses, paying taxes, enforcing contracts, etc. The researcher then computed an average of the rankings in the Ease-of-Doing-Business report and the Euromoney’s Country Risk Index. The averaged ranking obtained the market risk index.
The model also takes into account the cultural and geographic distances between the parent country and the emerging markets. The researchers developed a geographical distance proxy based on the time taken to travel to the various countries under review. This geographical distance proxy remains constant because the countries geographic positions do not change. The researchers also developed a formula for computing cultural distances. The formula uses variables of cultural difference developed by previous studies. Finally, the researchers computed an average of the geographical-and-cultural-differences ranking indices to obtain a distance index.
The researcher had computed three ranking tables of the various opportunities and risks involved in internationalization in each of the countries. These three tables include the market-opportunity ranking table, the market-risk ranking table, and the geo-cultural-distance ranking table. However, the researchers used the results of the survey to generate a compiling ratio for the three tables. The compiling ratio generates a table of averaged indices that depict the index of attractiveness for entry modes into emerging markets. According to Aliouche and Schlentrich, the results of the survey elected that the compiling ratio of market-opportunity, market-risk, and distance as 50:40:10 percent, respectively. The researchers used this compiling ratio to generate the final international franchise expansion index, which indicates the ranking of emerging markets’ attractiveness to USA companies.
Analysis of the China, Taiwan, Hong Kong, and Korea Markets for Franchising TSC
The Adjusted Total Urban Market Potential Method
GPD Per Capita Income Adjusted To Savings
The first step of Alon’s framework is to adjust the GDP per capita income according to the countries domestic savings data. Therefore, this step requires two forms of data, which are the GDP per capita and domestic savings data. The World Bank updates both of these sets of data annually. Therefore, the analysis table below indicates the information obtained from the recent updates by the World Bank. Also, the following adjustment formula computes the Per capita income adjusted to savings from the two sets of data.
I
Emerging market
GDP per capita
Domestic Saving (percentage of GDP)
Per capita income Adjusted to savings
China
9770.8
46.6
4553
Korea
31362.8
35.3
11071
Taiwan
87208.5
66.8
58255
Hong Kong
48675.6
21.8
10611
Table of per capita income adjusted to savings (World Bank a; World Bank b)
Income Spending On Service Adjustment to Savings
The next step is computing the proportion of the adjusted incomes that the respective countries spend on service. The computation requires data that shows the percentages of GPD resulting from the service industry (Park, and Noland). The adjusted income spending for service industry computation uses the following formula.
Emerging market
Service (percentage of GDP)
Income spending on Service adjusted to savings
China
40
1821
Korea
55
6089
Taiwan
62
36118
Hong Kong
90
9550
The table of income spending on service adjusted to savings (Park, and Noland)
Total Urban Market Potential Spending On Service
The final step involves computing the total potential of the emerging markets adjusted to the income spending on the service industry. The information used for this computation is the most recent regarding populations in Asia (WorldOMeter). The following formula is useful in computing the total urban market potential adjusted to income spending on the service industry.
Emerging market
Total Population
Urban population (percentage of total)
Total Urban Market potential
China
1,439,323,776
59
1546.39 billion
Korea
25,778,816
62
97.3 billion
Taiwan
23,816,775
100
860.2 billion
Hong Kong
7,496,981
100
71.6 billion
The total urban market potential with data from WorldOMeter and World Bank (WorldOMeter; World Bank c)
Composite Index Score Method
Market Opportunity Index
The first computation is the market opportunity index, which uses the following formula.
Emerging market
Population (million)
rank
GDP Per capita
Rank
Composite score
China
1334.3
1
6378
77
39
Korea
48.7
26
26730.2
27
26.5
Taiwan
23.2
46
28585.4
25
35.5
Hong Kong
7.1
91
41902.3
4
47.5
Table of Market opportunity index (Aliouche, and Schlentrich)
Market Risk Index
Aliouche and Schlentrich also computed the market risk index of all emerging economies. The following table indicates the comparison between the four emerging markets.
Market
Political-economic risk rank
Legal risk rank
Composite score
China
36
69
52.5
Korea
34
18
26
Taiwan
27
40
33.5
Hong Kong
20
3
11.5
Market risk index (Aliouche, and Schlentrich).
Distance index
The authors also computed a table for the distance indices of all emerging markets. According to Aliouche and Schlentrich, the distance indices of Korea, China, Taiwan, and Hong Kong are 101.0, 84.5, 74.5, and 71.0, respectively. These indices related to the distance that the emerging markets are away from the USA market.
The International Franchise Expansion Index
The authors finally computed the international franchising index that indicates the order of expansion for a service company based in the USA, such as Tropical Smoothie Café. The table below indicates the order of preferred franchising for the four emerging markets. The researchers used the following formula to compute the composite index scores, which applied the ratio obtained in the survey.
Market
Market opportunity index
Market risk index
Distance index
Composite index
Korea
10
21
116
25.0
Taiwan
17
28
70
26.7
Hong Kong
33
13
65
28.2
China
21
51
84
39.3
The international franchise expansion index (Aliouche, and Schlentrich)
Selection of Most Suitable Emerging Market for Tropical Smoothie Café
Recommendation for TSC
The two frameworks develop a differing analysis of the four emerging markets. According to Alon’s method, China is the most lucrative emerging market for tropical smoothie café expansion. The Chinese market has a total urban market potential of more than 1.5 trillion dollars. This potential market indicates an adjusted computation of China’s income spending on service products. The method also showed that Taiwan was the second most lucrative market, followed by Korea and Hong Kong. However, according to Aliouche and Schlentrich’s computation, Korea is the most lucrative market for TSC’s franchise. The composite index score for Korea was 25.0. This score indicates the ease of setting up and operating in the emerging market compared to all other markets in the world.
In my opinion, Aliouche and Schlentrich’s recommendation is better for franchising decision-making of Tropical Smoothie Café. Alon’s method indicates the size of untapped markets in the respective countries. However, the composite score index indicates the ease of setting up and operating in the emerging markets. I believe that the ease of setting up and operating in a foreign market is more crucial than the possibility of earning more. The company may interpret the total urban market potential table as a market opportunity while the composite index as an indicator of the possibility of success. Therefore, the composite index table is better at indicating the possibility of Tropical Smoothie’s success in the respective markets.
Shortcomings and suggestions for improving both frameworks
Both frameworks have a few shortcomings. In Alon’s method, he does not account for other factors that may affect the operations of the company within those emerging markets, such as politics, language, religion, etc. Additionally, the method does not consider the competition in those emerging markets, which may also influence the company’s success in each market. Future researchers can improve this method by adding more variables to compare additional factors.
The Aliouche and Schlentrich framework indicates the ease of operating in the respective markets. However, the framework focuses only on macro-environmental factors, such as Political, demographic, social-cultural factors, etc. Future improvements should include micro-environmental factors, such as labor. Also, the model uses the formula generated from a survey to compile the composite index scores. The formula cannot be accurate because it based on the perspective of a sampled group of respondents. Therefore, to improve the approach, future researchers should develop a better compiling formula based on valid and empirical studies.
Works Cited
Aliouche, E. Hachemi, and Udo A. Schlentrich. “Towards A Strategic Model Of Global Franchise Expansion”. Journal Of Retailing, vol 87, no. 3, 2011, pp. 345-365. Elsevier BV, doi:10.1016/j.jretai.2011.01.004. Accessed 15 Mar 2020.
Alon, Ilan. “Executive Insight: Evaluating The Market Size For Service Franchising In Emerging Markets”. International Journal Of Emerging Markets, vol 1, no. 1, 2006, pp. 9-20. Emerald, doi:10.1108/17468800610644979. Accessed 15 Mar 2020.
Park, Donghyun, and Marcus Noland. “Developing the Service Sector for Asia”. Cornell University ILR School, 2013, https://digitalcommons.ilr.cornell.edu/cgi/viewcontent.cgi?referer=https://www.google.com/&httpsredir=1&article=1321&context=intl. Accessed 16 Mar 2020.
TFC. “Our Story: Tropical Smoothie Cafe”. Tropicalsmoothiecafe.Com, 2020, https://www.tropicalsmoothiecafe.com/our-story/. Accessed 15 Mar 2020.
World Bank a. “Gross Domestic Savings (% Of GDP)”. Data.Worldbank.Org, 2019, https://data.worldbank.org/indicator/NY.GDS.TOTL.ZS. Accessed 16 Mar 2020.
World Bank b. “GDP Per Capita (Current US$) Asia And Europe”. Data.Worldbank.Org, 2019, https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=Z4-8S-Z7. Accessed 16 Mar 2020.
World Bank c. “Urban Population (% of the total population)”. Data.Worldbank.Org, 2019, https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS. Accessed 16 Mar 2020.
WorldOMeter. “Asian Countries By Population (2020)”. Worldometers.Info, 2020, https://www.worldometers.info/population/countries-in-asia-by-population/. Accessed 15 Mar 2020.