Market Research For Footwear Business Targeting Kent Institute Students

Research Objectives

Fashion and textile business is one of the lucrative businesses in Australia. Fashion industry supplies variety of products that are consumable by every individual living on this planet. Some of the common types of fashion products available around the globe are apparel, footwear, traditional, formal wear, cosmetics etc. (De Angelis, Ad?güzel and Amatulli, 2017). Due to the lucrativeness of the fashion business, footwear formed the center focus for market research targeting the students in Kent Institute. The business idea of opening the footwear business emerged with which it was to be implemented. In our business plan, being that the business idea was new and regarding the targeted prospective customers, location and situation of the business was one of the vital factors considered. One of the importance of proper business location is giving easy access to the business and also resulting to favorable competition with other businesses selling similar products (Kimelberg and Williams, 2013; Verhetsel et al, 2015). Keeping in mind the spending behavior of the students when on session, they mostly spend on food, stationeries and fashion. In regards to that, the decision for the location of the business was arrived at and agreed to be at the junction of Barrack Street and York Street. The main purpose of this report was to address the market requirements for the footwear business with Kent Institute students as the targeted customers.

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Some of the objectives to be met by this report are as stated below;

  1. To determine the most preferred type of shoes by the customers, Kent Institute students.
  2. To determine the price range for which the shoes would best sell
  3. To determine the average male and female shoe sizes to be supplied in the shop

This section will cover target population and sample size, data collection techniques and data analysis.

The targeted group by this research were the Kent institute students whose population was over 1000 students. Population is the total number of items targeted by the study (Colby and Ortman, 2017), in this case the Kent institute students. The students who participated in the research process were selected through random sampling. Sampling is the process of choosing the suitable sample size to be engaged in the study (Palinkas et al, 2015; Cleary, Horsfall, and Hayter, 2014). Sample is the proportion and a subset of the population of objects under study (Malterud, Siersma and Guassora, 2016). Sample size of 20 participants was used who were randomly selected from the population using the simple random sampling method. The most suitable sample size for the population in this research at a margin of error of 0.05 would be including 278 participants to participate in the data collection process. In that regard therefore, the size of the sample used was small which are associated with some of the advantages such as time saving and cost effectiveness as compared to when the large sample size would have been used (Hair, Sarstedt, Pieper and Ringle, 2012). The sample size used in this research was assumed to have fully represented the characteristics in the population and that the results could be relied on.

Data collection methods enable the researchers to collect or collate data from the participants in regards to the subject under investigation. Such methods include surveys, questionnaires, interviews, etc. From the randomly selected sample size of 20 respondents, the respondents were supplied with the questionnaires which were structured with the closed and open ended questions. The questionnaires were administered by the researcher in the data collection process where the researcher would provide clarity to the respondents where questions seem unclear. The questionnaires were structured with ten short answer questions. This method of data collection was appropriate since it saves respondents’ time and also allow them to give their responses in relaxed manner. Where multiple choices were available for some of the questions, the participant was required to pick only one choice out of the multiple choices. Resulted data from this method depending on the nature and structure of the questions were both quantitative and qualitative. Closed ended questions resulted to categorical variables whereas open ended questions resulted to numerical continuous variables.

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Methods of Data Collection

The data analysis methods applied in this research were frequency and descriptive statistics in order to draw meaning from the data. Both excel and SPSS were the statistical software used in the analysis of data in the representation of data on the tables and graphs for easy understanding and interpretations. Categorical variables were coded with numerical values and their scale measures were nominal with numerical variables taking scale measurement. Examples of the categorical variables from the dataset were; gender, Shoe-type, shoe-color, official-casual, buy-interval and shoelaces. On the other hand, numerical variables were; shoe-size, max-cost and min-cost. The dataset was as in the table below;

Table 1: Data in the dataset

Gender

Age

Shoe_type

Shoe_size

Max_cost

Min_cost

Shoe_color

Official_casual

Buy_interval

Shoelaces

1

2

4

38

20

5

2

1

6

1

2

1

2

36

18

5

2

1

2

2

2

2

1

38

24

12

2

1

4

2

1

2

2

42

40

20

2

2

5

1

1

3

2

40

34

10

2

1

6

2

1

4

2

43

30

12

3

2

6

2

2

3

2

38

22

9

3

2

5

2

1

3

2

44

21

9

2

2

5

1

2

3

3

36

28

10

3

2

2

1

2

3

2

33

30

15

1

2

3

1

2

4

3

38

20

10

2

1

3

2

2

4

1

36

50

20

1

1

4

2

1

2

1

45

48

22

1

1

6

2

1

5

1

40

45

18

1

1

6

2

2

5

1

40

48

15

2

2

4

2

2

4

2

42

50

25

2

1

3

2

2

4

1

42

50

21

3

1

2

2

1

3

2

44

36

9

4

2

3

1

1

3

2

45

40

5

4

2

3

1

1

5

2

44

30

10

2

1

5

2

Figure 1

For the age of the respondents, 5% represented those the age of (<18) years, 20% represented those student respondent who responded to have age of (18-20) years, 35% represented the students who responded to have age of (21-23) years, 25% of the respondents represented those students who responded to have the age of (24-26) year and lastly, 15% of the students respondents stated that they had age of (>26) years. From the figure 1, majority of the students were of age 21-23 year which was represented in the graph while the least represented age was <18 year showing that from the sample, the least number of students had their age in that age bracket.

Figure 2

For the question on the most preferred type of shoes by the students from the sample, rubbers closed shoes was preferred by 30% of the students, leather closed shoes was represented by 55%, rubbers open shoes was preferred by 10% of the students sampled and lastly, leather open shoes was preferred by 5% of the sampled students. From the data, the most preferred type of shoes was closed leather shoes as it was highly represented by 55% of the entire sample and the least preferred type of shoes was leather open shoes since it was least represented in the sample i.e. (5%).

Figure 3

White colored shoes were preferred by 20% of the students in the sample, black colored shoes was preferred by 50% of the students in the sample, brown colored shoes was preferred by 20% of the student covered in the sample and other shoe colors were preferred by 10% of the students in the sample. From that therefore, the most preferred shoe color by the students as from the sample was black with other colors apart from white and brown least preferred.

Figure 4

The frequency at which the sampled students were buying shoes across the year was; 15% bought shoes on monthly basis, 25% of the students bought the shoes in every 3 months, 15% of the sampled students bought shoes every 4 months, 20% bought shoes every 6 months and lastly, 25% of the students in sample bought shoes annually. This information was important to consider when deciding for the mount of footwear stock to maintain in the shop. Majority of the students bought shoes in every three months and annually as they were represented by the same highest percentage (25%) of responses.

Targeted Population

Figure 5

Thirty five percent of the student respondents in the sample preferred shoes with shoelaces while the remaining 65% of the students preferred the shoes without shoelaces. From these results, the shoe business is supposed to supply more of the shoes without shoelaces than those with shoelaces.

Table 2: Descriptive Statistics

N

Mean

Std. Deviation

Variance

Max_cost

20

34.2000

11.47354

131.642

Min_cost

20

16.1000

4.89791

23.989

Shoe_size

20

40.2000

3.48833

12.168

Valid N (listwise)

20

The mean value of the maximum cost students could spend on shoes was 34.2 with the standard deviation and variance of 11.47654 and 131.642 respectively. The least amount of money the students could ever incur on shoes from the data had the mean, standard deviation and variance of 16.1, 4.89791 and 23.989 respectively. The mean shoe size from the sample data was 40.2 with 3.48833 deviations from the mean and the variance of 12.168.

Table 3: Grouped shoe size summary

Gender

Male

Female

Mean

Maximum

Minimum

Mean

Maximum

Minimum

Shoe_size

42.50

45.00

38.00

37.90

42.00

33.00

The mean shoe size for the male respondents from the sample was 42.5 with the minimum size of 38 and the maximum shoe size of 45. On the other hand, the mean shoe size for female student respondents in the sample was 37.9 with minimum and maximum of 33 and 42 respectively. From the result in table 3, most of the male shoes should be of size 42.5 and maintained in the range between 38 and 45 in the shop with that of ladies mostly being 37.9 and others maintained in the range between size 33 and 42.

Margin of errors at 95% confidence level

Shoe size

Maximum cost

Minimum cost

Z (95%)

1.96

Z (95%)

1.96

Z (95%)

1.96

standard error

0.780013

Standard Error

2.565561

Standard Error

1.355107

Margin of Error

1.528826

Margin of Error

5.0285

Margin of Error

2.65601

The margin of error for shoe size, maximum cost incurred on the shoes by the students and the minimum cost incurred on the shoes by the students was determined at 5% level of significance. The margin of error for shoe size was 1.528826 at 95% confidence level, that of the maximum cost was 5.0285 and finally, that of the minimum cost that could ever be incurred by the students was 2.65601.

Confidence intervals at 95% confidence level

Shoe size

Maximum cost

Minimum cost

Upper limit

41.72883

Upper limit

39.2285

Upper limit

15.75601

Lower limit

38.67117

Lower limit

29.1715

Lower limit

10.44399

The calculated confidence interval for the variables were; shoe size (38.67117 – 41.72883), that of maximum cost was A$(29.1715 – 39.2285) and lastly, confidence interval for minimum cost was A$(10.44399 – 15.75601).

Conclusion

It can therefore be concluded from the results that the most preferred shoe type was leather closed shoes. The price range from the results at which the shoes could best sell was at the interval maximum cost of A$(29.1715 – 39.2285) and the minimum cost of A$(10.44399 – 15.75601). It is then recommended that the business should consider supplying more of leather closed shoes and the shoes supplied should have the prices within the maximum and minimum cost interval limits.

References

Cleary, M., Horsfall, J. and Hayter, M., 2014. Data collection and sampling in qualitative research: does size matter?. Journal of advanced nursing70(3), pp.473-475.

Colby, S.L. and Ortman, J.M., 2017. Projections of the size and composition of the US population: 2014 to 2060: Population estimates and projections.

De Angelis, M., Ad?güzel, F. and Amatulli, C., 2017. The role of design similarity in consumers’ evaluation of new green products: An investigation of luxury fashion brands. Journal of Cleaner Production141, pp.1515-1527.

Hair, J.F., Sarstedt, M., Pieper, T.M. and Ringle, C.M., 2012. The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long range planning45(5-6), pp.320-340.

Kimelberg, S.M. and Williams, E., 2013. Evaluating the importance of business location factors: The influence of facility type. Growth and Change44(1), pp.92-117.

Malterud, K., Siersma, V.D. and Guassora, A.D., 2016. Sample size in qualitative interview studies: guided by information power. Qualitative health research26(13), pp.1753-1760.

Palinkas, L.A., Horwitz, S.M., Green, C.A., Wisdom, J.P., Duan, N. and Hoagwood, K., 2015. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research42(5), pp.533-544.

Verhetsel, A., Kessels, R., Goos, P., Zijlstra, T., Blomme, N. and Cant, J., 2015. Location of logistics companies: a stated preference study to disentangle the impact of accessibility. Journal of Transport Geography42, pp.110-121.