Retail competetion and Consumer Choice

8/7/2019 Retail competetion and Consumer Choice

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Introduction

This report is intended to study some of the consumer shopping habits. The report is based on

statistical analysis of qualitative data.

About the Data

The data used in this report is based on the research named Retail Competition and Consumer

Choice. The data collected between 2002 and 2004 in Portsmouth, UK. Principle Investigators are:

Ian Clarke (AIM and Lancaster University Management School), Peter Jackson (University of

Sheffield) and Alan Hallsworth (Manchester Metropolitan University). The research funded by the

Economic & Social Research Council (ESRC). The distributer of the data is UK Data Archive,

University of Essex. The data downloaded from Economic and Social Data Services website.

The data collected in four stages, producing two quantitative and two qualitative data sets. Phase I

data collected by interviewing 2,515 consumers at the main food stores (Seven stores at time of the

research). Focus of this phase was exploring characteristics of shopper group, shopping travel time

and mode and shopping behaviour. Phase II was an attitudinal survey consist of distributing 2,150 to

be done at home. Focus of this phase was exploring views on grocery shopping, choice criteria and

attitude to particular stores. 430 participants responded to the survey by resending back the filled

questionnaires.

The scope of this report is to analyze the quantitative data only (Phase I and II). The questionnaires

used and raw data sets can be found in appendix.


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up to 24 25-34 35-44 45-60 over 60 Total Average

Male 2 2 13 19 45 81 19.29

Female 3 34 72 114 116 339 80.71

Total 5 36 85 133 161 420 100.00

Another characteristic can be compared between the two sets is the household income level. In the

first set interviewee are been asked directly to describe themselves. On the other hand, living area is

been used to categorized the respondents of second data set.

FrequencyPercent

low income 339 13.5

low/middle Income 894 35.6

middle Income 1004 40.0

high Income 64 2.5

middle/high Income 130 5.2

very rich 79 3.1

Total 2510 100.0

Frequency Percent

low income/Paulsgrove 99 23.0

low/middle/ Purbrook 93 21.6

middle /Drayton 64 14.9

high /Cavendish 84 19.5

middle/high/ Farlington 39 9.1

very rich 51 11.9

Total 430 100.0

Store visitor income level frequency distribution In house respondent income level frequency

distribution


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Most of the store visitors are middle income shoppers. The cumulative of middle and low/middle

indicate more than 75% of the costumers are falling in these two categories. The second frequency

distribution proves that the questionnaire was distributed very well, or at least the responses received

from various level of income.

For continuous variable the normality for the data is assessed. In store data set contains two

continuous variables: money spent on food and money spent on other shopping in the store. In house

data doesnt contain continuous data. Following is the normality assessment of two variables, money

spent on food and money spent on other:

Descriptives Statistic Std. Error

Money spent on food Mean 32.79 .593

95% Confidence Intervalfor Mean

Lower Bound 31.63

Upper Bound 33.96

5% Trimmed Mean 29.97

Median 25.00

Variance 884.371

Std. Deviation 29.738

Skewness 1.474 .049

Money spent on other Mean 2.95 .207

95% Confidence Interval

for Mean

Lower Bound 2.54

Upper Bound 3.35

5% Trimmed Mean 1.35

Median .00

Variance 108.252

Std. Deviation 10.404

Skewness 12.038 .049

Both variables has significantly high positive Skewness, this means scores from both variables are

clustered to the left. This means both of the variables are not normally distributed. Reporting the mean

gives a better idea about the data. Looking at the histograms below will give you a better idea.


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The histograms showed the actual distribution of the two variables, its obviously not normal. This

also supported by an inspection of the normal probability plot. Following the plot for both of the

variables:


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Looking at the histogram of money spent on other, you will notice that a lot of scores are equal to

zero. Therefore, we can create another variable of total money spent. Following the descriptive

statistics for the new variable:

Statistic Std. ErrorSpentTotal Mean 35.7391 .64713 95% Confidence Interval

for MeanLower Bound 34.4701 Upper Bound 37.0081

5% Trimmed Mean 32.5853 Median 27.0000 Variance 1053.229 Std. Deviation 32.45348 Interquartile Range 39.00 Skewness 2.144 .049

The histogram shape doesnt look different from money spent on food. However, it contains less

peakedness

Last descriptive observation of the data would be the difference between male and female shopper in

terms of spending. The new variable Total Spent is used here. Following bar graph gives a rough

idea:


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We cannot neglect the fact that females representing more than 70% of the in store interviewee.

However this give an indication those female shoppers are more likely to spend money on food more

than male shoppers.


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Tests of the Data

In this section of the report the data set is to be tested to conclude some findings. Tests used are

correlation,

Test of Correlation

First test is the correlation between the total money spent and time spent on the store. The null

hypothesis is theres no correlation (correlation coefficient is zero). The alternative hypothesis is that

the correlation coefficient is not zero. To summarize:

H0: theres no correlation, correlation coefficient is zero.

Ha: correlation coefficient is not zero

Running the Pearson Two-Tailed Correlation test in SPSS give the following output:

Correlations

SpentTotal Time SpentSpentTotal Pearson Correlation 1 .570**

Sig. (2-tailed) .000

N 2515 2509

Time Spent Pearson Correlation .570** 1

Sig. (2-tailed) .000

N 2509 2509

**. Correlation is significant at the 0.01 level (2-tailed).

First thing to check is the N-value which is number of cases, this indicates only 6 cases are excluded.

Secondly, we check the r-value which indicates the correlation coefficient. In this case the r-value is

not equal zero. Therefore, the null hypothesis is rejected and the alternative hypothesis is suggested to

be true. Since the r-value is positive, its suggested that the correlation is positive. The significant

level is .000, this means were sure at p < .0005. In simple English, were quite sure that shoppers

who stay longer in the store tend to spend more money.

Mann-Whitney U Test

Second test on the data will be Mann-Whitney U test. This is a non-parametric test used to compare

the median of two independent groups on a continuous measure. The test is used her to determine

whether female shoppers tend to spend more money in total. The null hypothesis is that both male and

female shoppers have the same median. The alternative hypothesis one of the two group tend to spend

more. To summarize:

H0: Theres no difference, the two groups has the same median.

Ha: The two group median is not the same


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Running Mann-Whitney U test in SPSS gives the following output:

Test Statisticsa

SpentTotal

Mann-Whitney U 544312.000

Wilcoxon W 807487.000

Z -5.351

Asymp. Sig. (2-tailed) .000

a. Grouping Variable: Respondent gender

Ranks

gender N Mean RankSum ofRanks

SpentTotal Male 725 1113.78 807487 Female 1739 1282 2229393

Total 2464

Obtaining the median scores for each group gives the following output:

Respondent gender N Median

Male 725 22.0000

Female 1739 30.0000

Total 2464 27.0000

The value of r can be calculated as the following: r = Z/SQRT(N) ==> r = -5.351/SQRT(2464)

==> r = -0.1078. Therefore, the Mann-Whitney U test revealed theres a significant difference

between the two medians. The null hypothesis is rejected and the alternative hypothesis is suggested

to be true.

One-way ANOVA Test

The third test to be used is the one-way ANOVA, its used here to determine if theres a difference in

total money spent for different age group of the sample. To summarize:

H0: Theres no difference, all the groups has the same mean. 1 = 2 = 3 = 4 = 5 = 6

Ha: The group means are not the same.

Following is the descriptive statistics:

N Std. Deviation Std. Error

95% Confidence Interval forMean

Minimum Maximum

Lower Bound Upper Bound

up to 24 142 24.25 28.77 2.41 19.48 29.03 .00 210.00

25-34 301 37.70 31.32 1.81 34.15 41.25 .00 178.00

35-44 577 45.61 40.51 1.69 42.30 48.92 .00 435.00

45-60 835 36.76 31.59 1.09 34.62 38.91 .00 190.00

over 60 654 27.17 22.40 .88 25.45 28.89 .00 134.00

Total 2509 35.70 32.45 .65 34.43 36.97 .00 435.00

Running one-way ANOVA gives the following output:


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Sum of Squares df Mean Square F Sig.

Between Groups 124963.376 4 31240.844 31.090 .000

Within Groups 2516114.935 2504 1004.838

Total 2641078.311 2508

From the above we can conclude that theres a significant difference among the mean scores.

Therefore, the null hypothesis is rejected and the alternative hypothesis is suggested with probability

of error near to zero. Following table (multiple comparisons) shows the significant differences in full

details:

AgeGroup Age Group

MeanDifference (I-J) Std. Error Sig.

95%confidenceInterval

Lower Bound Upper Boundup to 24 25-34 -13.45* 3.23 .000 -22.26 -4.64

35-44 -21.36* 2.97 .000 -29.46 -13.25

45-60 -12.51* 2.88 .000 -20.37 -4.66

over 60 -2.92 2.93 .858 -10.93 5.09

25-34 up to 24 13.45* 3.23 .000 4.64 22.26

35-44 -7.91* 2.25 .004 -14.06 -1.76

45-60 .94 2.13 .992 -4.88 6.75

over 60 10.53* 2.21 .000 4.50 16.55

35-44 up to 24 21.36* 2.97 .000 13.25 29.46

25-34 7.91* 2.25 .004 1.76 14.06

45-60 8.84* 1.72 .000 4.16 13.53

over 60 18.44* 1.81 .000 13.49 23.38

45-60 up to 24 12.51* 2.88 .000 4.66 20.37 25-34 -.94 2.13 .992 -6.75 4.88

35-44 -8.84* 1.72 .000 -13.53 -4.16

over 60 9.59* 1.66 .000 5.07 14.11over 60 up to 24 2.92 2.93 .858 -5.09 10.93

25-34 -10.53* 2.21 .000 -16.55 -4.50

35-44 -18.44* 1.81 .000 -23.38 -13.49

45-60 -9.59* 1.66 .000 -14.11 -5.07

*. The mean difference is significant at the 0.05 level.

Conclusion

The tests experimented in this report only described some of the data characteristics. Relevant

information like the size of shopping group and time travelled for shopping werent tested. There is a

positive correlation between the time spent in store and total money spent. Females appeared to spent

more money in shopping in comparison to males of the research sample. Finally different age groups

seem to have significantly different spending habits.

Retail competetion and Consumer Choice

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