Variables Definition

We tried to address the impact of demographic variables, differences between stores, competition relationships on the convenience store density in Taipei in this research. The detailed definition of each variable used in this model would be explained as follows.

4.3.1 Store numbers

In order to catch the extremely high convenience store density in Taipei, we use the number of convenience stores in the defined market as the index to evaluate market density;

therefore, we calculate the four brands of convenience stores inside each defined market to be the dependent variable in this model.

4.3.2 Population

We suppose that the population live in the defined market should be the principal customers to the convenience store, which will influence the new store opening decisions made by firms; hence, according to Doctor Jung, Doctor Sun, and Doctor Lee suggested, we collect the population data published by the regional household registration office and consider the neighborhoods level populations of the center 7-Eleven store as one variable in the model.

4.3.3 Household size

We supposed that how many people in one family was one of the key factors that would affect the demand of convenience stores in the market; in other words, the size of family would determine the willingness of habitants to consume in the convenience stores and further affect the store numbers in the defined market. Hence, we defined the household size variable as total population numbers divided by total registered household numbers and implied it into our model.

4.3.4 Male ratio

To figure out whether the sexual differences in each market would be an obvious factor

that impacted the density of convenience stores, we calculated the percentage of male population numbers to clarify the assumption.

4.3.5 Average age, the ratio of young population and elder population

Since purchasing daily necessities in the convenience stores is a new trend among people lived in Taiwan, we believed that the age composition would be a key point to determine the operation of new stores in defined markets, thus influence the density of convenience stores. We collected the age data from District Administrations in Taipei and derived the average age of population and calculated the ratio of population under 15 years old and over 65 years old as variables related to age distribution in this model.

4.3.6 Education years

According to the annual statistics report published by Department of Education, Taipei City Government, we could get the graduated or studied but no degree population numbers.

We assume people who graduated from the specified level of school got the full standard educational years and people who did not graduate but ever enrolled the specified level of school got half of the standard educational years to construct the average educational years of population by calculating weighted average years.⁵

5 To put it differently as an illustration, people who graduated from elementary school would obtain 6 years as his/her educational years while people who had enrolled elementary school but did not graduate would obtain 3 years as his/her educational years; people who graduated from junior high school would obtain 9 years as his/her educational years while people who had enrolled junior high school but did not graduate would obtain 7.5 years as his/her educational years; people who graduated from senior high school would obtain 12 years as his/her educational years while people who had enrolled senior high school but did not graduate would obtain 10.5 years as his/her educational years; people who graduated from college would obtain 16 years as his/her educational years while people who had enrolled college but did not graduate

4.3.7 Married ratio

The marital status might have impact on the store number of convenience store because we assumed that people who did not live with his/her family would have more chance to buy food, beverage or other daily necessities in a convenience store since the package of commodities in other physical channels were too large to utilize by one person. Due to this reason, we arranged married ratio in the defined market by dividing married population into total population.

4.3.8 Average income of taxation entities and average consumption of households Obtained from National Taxation Bureau of Taipei, Ministry of Finance, we arranged the income and consumption data of 12 districts in Taipei city as our independent variables.

4.3.9 Competition and square of competition

In order to determine the competition relationships among different chain convenience store brands, we calculated the 7-Eleven store ratio of total stores included in a specified market as the variable which described the competition situation of this region. Though it has different meanings to one market with 2 7-Eleven stores over total 4 convenience stores and the other market with 1 7-Eleven store over total 2 convenience stores, because of the

would obtain 14 years as his/her educational years; people who graduated from two-year specialized school would obtain 14 years as his/her educational years while people who had enrolled two-year specialized school but did not graduate would obtain 13 years as his/her educational years; people who graduated from five-year specialized school would obtain 17 years as his/her educational years while people who had enrolled five-year specialized school but did not graduate would obtain 14.5 years as his/her educational years; people who graduated from graduate institute would obtain 18 years as his/her educational years if he/she studied master degree, 23 years if he/she studied doctor degree ,whereas people who had enrolled graduate institute but did not graduate would obtain 17 years as his/her educational years if he/she studied master degree, 20.5 years if he/she studied doctor degree.

limitations of data collection, we can only assume these two conditions are the same in our regression model. Besides, taking account of the findings from Huang. (2014), we make the square of competition variable to see the nonlinear relationship between store density and competition.

4.3.10 Service ratio

The fragmentation of convenience stores had gradually gone from convenient goods to service-oriented commodities; therefore, there are many kind of services provided by different brand of convenience store recently. To capture this feature of convenience stores, we try hard to create a service index that could explain these differences of each store. In the beginning, we try to separate the services items into the same classifications and check whether the four brands of convenience stores offer that kind of service or not; however, since the open information on the official website were limited and we have no access to get more detailed data about the service conditions in each store, we can only use the existing data to build a relatively reasonable variable to catch the service differences between different stores. As a result, we take the provided service items of each store divided by total service item provided by its brand, and get an index to present the service differences in each store in the market.

4.3.11 Store size, Road width, Sidewalk width and Corner

We collect the practical situations of convenience stores in Taipei city by searching Google map website and perform four types of variables from the information. First of all, we distinguish the size of convenience store into small, middle and large, three kinds of degrees, and give these three degrees number 1, 2, and 3 which stands for the different size

of each store. Secondly, we observe the driveways of the road built in front of each store, and record the number of driveways to represent the width of the road. Thirdly, we also observe the sidewalks in front of each store and classify them into four levels. If there is no sidewalk in front of the shop, we give number 0, and then 1, 2, 3 sequentially by the width of sidewalk. Finally, whether each store located on the corner or not can be seen obviously in the Google map website, so we take this as one variable to see the influence.

On the other hand, we classify different brand of shops into three parts, center stores, 7-Eleven but not center stores and all the other three brands stores to separate the variant impacts on store density. We calculate the distance between each store and the center in each defined market and use the inverse number as the weight of each store. Multiplying store size, road width, sidewalk width and corner by the weight of each store, we can gain the weighted variables that we want to use in our model.⁶

4.3.12 Landmarks

The landmarks lied in the market would be an important factor that influenced the density of convenience stores since the stream of consumers around the landmarks would be higher. We adopted elementary schools, junior high schools, senior high schools, colleges, MRT stations, railway stations, Taiwan high speed railway stations, airports, hospitals, and the most famous spots voted by tourists around the world in Taipei as the main landmarks we tried to explore, whereas we calculated the number of landmarks in the defined market to be the landmark variable.

6 For example, if there is a Hi-Life shop with small size, six driveway on the road, middle sidewalk, locates on the corner of the block and the distance to center store is 10 meters. The variable store size will be 1*1/10=0.1, road width will be 6*1/10=0.6, sidewalk width will be 2*1/10=0.2 and variable corner will be 1*1/10=0.1.

4.3.13 Supermarkets

Because the commodities sold and the target customers in the supermarket were similar to the convenience stores roughly, we take the number of supermarket located in one defined market into account. The brands of chain supermarket we consider are Pxmart, Simplemart, Welcome, and Matsusei.

4.3.14 Perimeter, surface area, and neighbors of Thiessen polygon

While using Thiessen polygon method to differentiate markets, every polygon will have different perimeter and surface area which can be considered as decisive variables in our model. In addition, since Thiessen polygon is composed of many vertical lines to the connections of the reference points, the neighbors that each polygon has can be observed, and this variable can capture the competition relationship in 7-Eleven for us.