RESEARCH
METHOD
Effect Factors Analysis of
Chinese Consumers' Impulse Buying
Under the Background of Online Shopping
LEI, XI 2018475030
XINYAN, LIU 2018475038
YUTONG, LI 2018475039
CHUNFEI, SONG 2018475029
Content
01
02
03
04
Introduction & Literature Review
Questionnaire and variable explanation
Empirical Analysis
Conclusion and Limitations
Introduction & Literature Review
1.1 What is Impulse Buying
☺
Impulse buying is a sudden and powerful urge by consumer
to buy immediately (Beatty and Ferell 1998) and a spontaneous
desire to buy without deliberate consideration of why and for
what reason a person should have the product (Rook 1987).
Number of online shoppers in millions
1.2 Current Situation of Chinese Online Shopping
Number of online shoppers in
China from 2008 to 2018 (in
millions)
800
700
610.11
600
533.32
500
466.7
413.25
361.42
400
301.89
300
242.02
193.95
160.51
200
100
74
108
0
Resource: Statista 2019
1.3 Literature Review
⚫ Dholakia(2000) also found that women and men have great differences in online impulsive
consumption. Compared with women, men have a significantly lower probability of impulsive consumption.
⚫ Disposable money is the promoter of the impulse buying behavior (Beatty and Ferrell, 1998), for it can
increase the purchase rights of each individual.
⚫ The amount of time available for a consumer would determine whether he or she would buy
impulsively. Due to the lack of time to browse the shopping site, consumers are likely to produce negative
emotions, thus reducing their tendency towards impulse buying behavior (Beatty and Ferrell, 1998).
⚫ Other’s companion may increase the possibility of impulse buying behavior (Luo, 2004). Comments
and persuasions from friends will also play a role in promoting or inhibiting impulse buying (Mai et al.,
2003).
⚫ Hadjali (2012) and other researchers have proved that promotions and price falls will arouse their
buying desire. Also, upgrading web designs, announcements and customer services, online
operators can persuade consumers to buy more in an indirect way.
⚫ Verhagen and Dolen (2010) believed that a good communication between online sellers and buyers
as well as efficient network navigation would stimulate their buying impulses behavior.
1.4 Factors That Influence Online Impulse Buying
Emerging overdraft facility
and installment
Combination of social
media & e-commerce
7 days no reason to
7
return and exchange
1.4 Factors That Influence Online Impulse Buying
(1)Internal factors: such as age, gender, education background,
monthly disposable income, occupation
(2)External factors: Online shopping festival, Free to refund and
exchange, Recommendations on social platform, Overdraft
facility, Aimless browsing
8
Questionnaire and Variable Explanation
2.1 Questionnaire Distribution
Time: from May 23 to May 29
Total: 225 copies
Platform: Wechat
2.2 Variable Explanation--Demographic Factors
14%
24%
 Gender
46%
 Age
54%
62%
 Occupation
MALE
 Education Background
FEMALE
10%
21%
 Monthly Disposable Income
44%
25%
≤22
15%
22-35
≥35
9%
16%
2%
21%
54%
8%
Senior high school and below Junior college
13%
Full-time Student
Civil servant
Corporate employee
Others
Self-employed
28%
Bachelor Degree
34%
≤1500
1501-3000
3001-5000
5001-10000
Master Degree and above
11
≥10000
2.3 Variable Explanation--External Stimulus Factors
 I am an impulsive buyer due to Online shopping festival
Strongly disagree/ Disagree/ Undecided/ Agree/ Strongly agree
 I am an impulsive buyer due to Free to refund and exchange
Strongly disagree/Disagree/Undecided/Agree/Strongly agree
 I am an impulsive buyer due to Recommendations on social platform
Strongly disagree/Disagree/Undecided/Agree/Strongly agree
 I am an impulsive buyer due to Overdraft facility
Strongly disagree/Disagree/Undecided/Agree/Strongly agree
 I am an impulsive buyer due to Aimless browsing
Strongly disagree/Disagree/Undecided/Agree/Strongly agree
✓ I am an impulsive buyer
Strongly disagree/Disagree/Undecided/Agree/Strongly agree
12
2.3 Variable Explanation--External Stimulus Factors
“ Online shopping festival ”
15.11%
Strongly Agree
25.78%
Agree
 “ Online shopping festival ”
35.11%
Undecided
14.22%
9.78%
Disagree
Strongly Disagree
0
10
20
30
40
50
60
70
80
90
“ Free to refund and exchange ”
 “ Free to refund and exchange ”
6.22%
9.33%
Strongly Agree
Agree
Undecided
Disagree
Strongly Disagree
0
10
20
30
40
50
28%
28%
28.44%70
60
“ Recommendations on social platform ”
 “ Recommendations on social
platform ”
9.33%
Strongly Agree
15.11%
Agree
22.22%
24.44%
Undecided
Disagree
13
Strongly Disagree
0
10
20
30
40
50
60
28.89%
70
2.3 Variable Explanation--External Stimulus Factors
“ Overdraft facility ”
7.11%
Strongly Agree
 “ Overdraft facility ”
13.78%
16%
Agree
Undecided
24%
Disagree
39.11
Strongly Disagree
0
10
20
30
40
50
60
70
80
90
100
“ Aimless browsing ”
7.11%
Strongly Agree
 “ Aimless browsing ”
14.22%
Agree
26.22%
23.11%
29.33%
Undecided
Disagree
Strongly Disagree
0
10
20
30
40
50
60
70
The Degree of online impluse buying
 The degree of online impluse
buying
3.11%
Strongly Agreed
12.45%
Agreed
27.11%
Undecided
41.78%
Disagreed
14
15.56%
Strongly Disagreed
0
10
20
30
40
50
60
70
80
90
100
Empirical Analysis
T-Test
ANOVA
Multiple Regression and Correlation
4.1 T-Test: Two-Sample Assuming Equal Variances(Gender)
F-Test Two-Sample for Variances（α=0.05）
Mean
Variance
Observations
df
F
P(F<=f) one-tail
F Critical one-tail
Male(1)
2.115384615
0.802091113
104
103
0.811819529
0.138666448
0.729075637
Female(2)
2.752066116
0.988016529
121
120
：
：
Two population variances are
approximately equal
Two population variances are
not equal
t-Test: Two-Sample Assuming Equal Variances（α=0.05）
Mean
Variance
Observations
Pooled Variance
Hypothesized Mean Difference
df
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
Male(1)
2.115384615
0.802091113
104
0.902140664
0
223
-5.013063401
0.00000055
1.65171532
0.00000109
1.970658961
Female(2)
2.752066116
0.988016529
121
There is no significant difference
：
：
between male and female
There is a significant difference
between male and female
4.1 T-Test: Two-Sample Assuming Equal Variances (Gender)
F-Test Two-Sample for Variances（α=0.05）
Mean
Variance
Observations
df
F
P(F<=f) one-tail
F Critical one-tail
Male(1)
2.115384615
0.802091113
104
103
0.811819529
0.138666448
0.729075637
Female(2)
2.752066116
0.988016529
121
120
Conclusion:
t-Test: Two-Sample Assuming Equal Variances（α=0.05）
Mean
Variance
Observations
Pooled Variance
Hypothesized Mean Difference
df
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
Male(1)
2.115384615
0.802091113
104
0.902140664
0
223
-5.013063401
0.00000055
1.65171532
0.00000109
1.970658961
Female(2)
2.752066116
0.988016529
121
There is a significant difference
between male and female with at
least 95% confidence.
Female is more likely than male to
make impulse buying online.
4.2 Anova: Single Factor (Age)
：
：
At least one of the treatment group means differs from the rest
Anova: Single Factor（α=0.05）
SUMMARY
Groups
Under 23(1)
23-35(2)
Above 35(3)
Count
55
139
31
ANOVA
Source of Variation
Between Groups
Within Groups
SS
22.1172068
201.7316821
Total
223.8488889
Sum
145
356
52
df
2
222
Average
2.6363636
2.5611511
1.6774194
Variance
1.2727273
0.8422479
0.5591398
MS
11.0586034
0.9087013
F
12.1696797
P-value
F crit
0.0000097 3.0365237
224
Conclusion:
We have at least 95% confidence to assume that there are significant differences in impulsive online
shopping behavior at different ages, consumers under the age of 23 are more likely to be impulsive
online, while those over the age of 35 are significantly less likely to behave like this.
4.2 Anova: Single Factor (Occupation)
：
：
At least one of the treatment group means differs from the rest
Anova: Single Factor（α=0.05）
SUMMARY
Groups
Student(1)
Civil servant(2)
Company employee(3)
Self-employed(4)
Others(5)
Count
99
28
56
19
23
ANOVA
Source of Variation
Between Groups
Within Groups
SS
6.3751452
217.4737436
Total
223.8488889
Sum
255
68
140
38
52
df
4
220
Average
2.5757576
2.4285714
2.5
2
2.2608696
Variance
1.1038961
1.2169312
0.9090909
0.5555556
0.7470356
MS
1.5937863
0.9885170
F
1.6123003
P-value
0.1721664
F crit
2.4126820
224
Conclusion:
There not enough evidence to assume that there are significant differences in impulsive online shopping
behavior at different occupations.
4.2 Anova: Single Factor (Education Background)
：
：
At least one of the treatment group means differs from the rest
Anova: Single Factor（α=0.05）
SUMMARY
Groups
High School/ Under High School(1)
Associate Degree(2)
Bachelor's Degree(3)
Master/PhD Degree(4)
Count
20
36
122
47
Sum
46
84
297
126
Average
2.3
2.3333333
2.4344262
2.6808511
Variance
1.3789474
0.7428571
1.0411191
0.9176688
MS
1.1535710
0.9972316
F
1.1567735
ANOVA
Source of Variation
Between Groups
Within Groups
SS
3.4607131
220.3881758
Total
223.8488889
df
3
221
P-value
0.3271571
F crit
2.6454505
224
Conclusion:
There not enough evidence to assume that there are significant differences in impulsive online shopping
behavior at different education backgrounds.
4.2 Anova: Single Factor (Monthly Disposable Income)
：
：
At least one of the treatment group means differs from the rest
Anova: Single Factor（α=0.05）
SUMMARY
Groups
Less than ￥1500(1)
￥1501-￥3000(2)
￥3001-￥5000(3)
￥5000-￥10000(4)
More than ￥10000(5)
Count
Sum
46
77
64
33
5
ANOVA
Source of Variation
Between Groups
Within Groups
SS
10.0553001
213.7935888
Total
223.8488889
97
205
154
86
11
Average
2.1086957
2.6623377
2.40625
2.6060606
2.2
Variance
0.7657005
1.1213260
0.7847222
1.2462121
1.2
4
220
MS
2.5138250
0.9717890
F
2.5868012
df
P-value
0.0378656
F crit
2.4126820
224
Conclusion:
We have at least 95% confidence to assume that there are significant differences in impulsive online shopping behavior at
different monthly disposable income, consumers with ￥1500-10000 are more likely to be impulsive online, while those
with less than ￥1500 or more than ￥10000 are significantly less likely to behave like this.
3.3 The multiple regression model
yො =0.141850778+0.292270664𝑥1 +0.132297341𝑥2 +0.260572118𝑥3 +0.067220161𝑥4 +0.100429441𝑥5
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.871922891
0.760249527
0.754775772
0.495034139
225
Critical Value:
F-test: F(0.05,5,219)=2.01
T-test: t(0.025,219)=1.972
ANOVA
df
Regression
Residual
Total
5
219
224
Coefficients
SS
MS
170.181012 34.03620239
53.66787693 0.245058799
223.8488889
Standard Error
F
Significance F
138.8899422 0.0000000000
t Stat
P-value
Lower 95%
Upper 95%
Intercept
0.141850778
0.101465
1.398026687
0.1635193579
-0.058122062 0.34182362
Online shopping festival
0.292270664
0.03963972
7.373176754
0.0000000000
0.214146509 0.37039482
Free to refund and exchange
0.132297341
0.03634615
3.639927186
0.0003402735
0.060664335 0.20393035
Recommendations on social platform
0.260572118
0.042217366
6.172154799
0.0000000032
0.177367795 0.34377644
Overdraft facility
0.067220161
0.037132934
1.810257177
0.0716265607
Aimless browsing
0.100429441
0.040777716
2.462851049
0.0145550648
-0.005963482
0.1404038
0.02006246 0.18079642
Conclusion and Limitations
4.1 Conclusion
Demographic
External
1. Significant difference between male and female.
1.
Positive influence
2. Significant differences at different ages and different
2.
Online shopping festival, free refund and exchange,
monthly disposable income.
3. No significant differences at different education
recommendations by celebrities on social platform and
aimless browsing are significant （α=0.05）
backgrounds and different occupations.
（α=0.05）
24
4.2 Limitations
➢ Our sample size is not large enough to generate a good representative for the
whole population.
➢ R²is only 0.76, which means that there are still set of factors affecting consumers’
impulsive buying that we did not take into consideration.
➢ The “extent” of impulsive buying is somewhat ambiguous and subjective.
25
RESEARCH
METHOD
Thank you
Reference
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