This study aimed to determine the impact of impulse buying on student behavior based on gender and age. An online questionnaire was completed by 62 business students. Descriptive statistics, t-tests, and ANOVA were used to analyze the data. The results showed no significant differences in impulse buying based on gender. For age, only one question showed a difference between ages 21-23 and 24-27, partially supporting the hypothesis regarding age differences. Overall, the study provided a learning experience for using statistical analysis but could be improved by expanding the sample.

1. IMPACT OF IMPULSE
BUYING ON STUDENTS
MBA-36(1)

2. Contents
Introduction
Aim and Objectives of study
Hypotheses
Research Methodology
Data Analysis;
Descriptive statistics
T-test
ANOVA
Findings
Limitation
Demographics
Importance of questions
Conclusion

3. Introduction
– Why we choose this topic?
– Emerging need
– Students are involved in Impulse buying even if they get
pocket money from their parents
– What we wanted to find?
– Significant Impact of Impulse buying on the basis of Gender
– Significant Impact of Impulse buying on the basis of our
Age Groups

4. About Questionnaire:
– We analyzed students behavior towards impulse
through online questionnaire conducted through
Google Docs.
– 62 respondents gave their valuable feedback.
– Through which we analyzed the results

5. AIMS & OBJECTIVES OF
THE STUDY
– The aim of this study was to determine the
impact of impulse buying on the behavior of
students on the basis of gender and age group.
The objectives were to analyze the differences
occurring due to gender and age in the impulse
buying behavior of students.

6. Hypothesis
– Ha:There is an impact of impulse buying on student
behavior on the basis of gender. [Rejected]
– H0:There is no impact of impulse buying on student
behavior on the basis of gender. [Accepted]
– Ha:There is an impact of impulse buying on student
behavior on the basis of age group. [Rejected, slightly
accepted]

7. Explanation
– According to our analysis, there was no any difference
when we compared our results with gender. Male and
female had comparatively no difference in terms of
impulse buying.
– When we compared our results on the basis of age group,
there was a no difference overall other than one group of
the item. Which means that there was nominal acceptance
otherwise the overall results showed the hypothesis to be
rejected.

8. Methodology
– The data was collected from Business students overall.
Both males and females of different age groups. The data
was analyzed using descriptive statistics and the
exploratory factor analyses.

9. Descriptive Statistics
-Descriptive statistics help us to simplify large
amounts of data in a sensible way. Each descriptive
statistic reduces lots of data into a simpler
summary
-They form the basis of virtually every quantitative
analysis of data
-Due to our data without constructs and every
question of it’s own type. We used descriptive
Statistics

12. Questions:
1) Did you buy any product without actually planning for it?
-Near Yes but Towards No. (1.18)
2) Does unexpected/unplanned buying happens more often when you
go for a purchase?
-Towards No. (1.39)
3) which factors do you consider more influencing in an
unexpected/unplanned purchase?
-Effective promotions. (2.92)
4) Do you think students like you are more engaged in impulse buying
nowadays?
-Neutral. (2.87)
5) Do you think price plays a vital role in developing impulse buying
behavior?
-Close to Yes option. (1.05)

13. Continue….
6) who do you consider your major reference group in achieving impulse
buying behavior?
-Family. (1.94)
7) How your reference group influence you towards impulse buying
behavior?
-which is they tell you that you will be socially accepted. (1.74)
8) which products do you most prefer while doing impulse/unplanned
buying?
-Apparels/ Accessories and Grocery Items. (2.52)
9) Which Factors do you consider more appealing in achieving Impulse
Buying Behavior?
-Emotional Appeals, going towards fear appeals. (2.23)
10) Do you think Impulse Buying among Students is increasing and it is a
main cause for marketers to achieve profits?
-Yes but going on towards No. (1.13)

14. T-test
– An independent-samples t-test is used when you want to
compare the mean score, on some continuous variable, for
two different groups of participants.

15. STEPS FOR
INTERPRETING t-test
– Step 1: Checking the information about the groups.
Check N value
Check code
– Step 2: Checking assumptions
Independent Samples Test output box gives you the results of Levene’s test for
equality of variances.
Sig. Value > o.o5 see line one (equal variances are assumed)
Sig. Value < o.o5 see line two (equal variances are assumed)
– Step 3: Assessing differences between the groups
If the value in the Sig. (2-tailed) column is equal or less than .05 (e.g. .03, .01, .001),
there is a significant difference in the mean scores on your dependent variable for
each of the two groups. • If the value is above .05 (e.g. .06, .10), there is no
significant difference between the two groups.

16. Independent Samples Test
Levene's Test
for Equality of
Variances t-test for Equality of Means
F Sig. T df
Sig. (2-
tailed)
Mean
Difference
Std.Error
Difference
95% Confidence Interval
of the Difference
Lower Upper
Q1 Equal variances assumed 2.561 .115 .802 60 .426 .079 .099 -.119 .277
Equal variances not assumed .784 50.474 .437 .079 .101 -.124 .283
Q2 Equal variances assumed 1.983 .164 .805 60 .424 .102 .126 -.151 .354
Equal variances not assumed .800 54.611 .427 .102 .127 -.153 .356
Q3 Equal variances assumed .106 .746 -.131 60 .896 -.054 .411 -.876 .768
Equal variances not assumed -.131 55.441 .896 -.054 .412 -.880 .772
Q4 Equal variances assumed .561 .457 1.853 60 .069 .425 .230 -.034 .885
Equal variances not assumed 1.855 56.328 .069 .425 .229 -.034 .885
Q5 Equal variances assumed 22.120 .000 2.058 60 .044 .111 .054 .003 .219
Equal variances not assumed 1.803 26.000 .083 .111 .062 -.016 .238
Q6 Equal variances assumed .075 .785 -.572 60 .570 -.148 .259 -.666 .370
Equal variances not assumed -.580 58.568 .564 -.148 .255 -.659 .363
Q7 Equal variances assumed .564 .456 -.008 60 .994 -.002 .276 -.555 .551
Equal variances not assumed -.008 58.253 .994 -.002 .273 -.549 .545
Q8 Equal variances assumed 9.945 .003 .792 60 .431 .201 .254 -.306 .709
Equal variances not assumed .756 44.043 .453 .201 .266 -.335 .737
Q9 Equal variances assumed .591 .445 -.242 60 .809 -.072 .297 -.666 .522
Equal variances not assumed -.245 57.905 .808 -.072 .294 -.661 .517
Q10 Equal variances assumed 17.731 .000 1.950 60 .056 .165 .085 -.004 .334
Equal variances not assumed 1.819 38.212 .077 .165 .091 -.019 .349

17. Interpretation
– In this case only question number 5 shows a little difference
between male and female group in gender wise.
– The t value in this output is 1.803 (which is less than 1.96). The
score sig. (2-tailed) is 0.083 (which is greater than 0.05),
therefore there is no significant difference between the means
of the gender group.
– The Mean Difference between the two groups is also shown in
this output, along with the 95% Confidence Interval of the
Difference showing the Lower value and the Upper value
because there are zero value exist, the lower value is -0.016
and the upper value is 0.238.

18. ANOVA
Statistical techniques specially designed to test whether the
mean of more than 2 quantitative population are equal.
ANOVA measures significant difference among at-least three
or more categories.
One-way ANOVA shows whether there are significant
differences in the mean scores on the dependent variable
across the three or more groups.
Post-hoc tests can then be used to find out where these
differences lie.
We use Tukey test when all pairs of sample means are to be
tested.

21. Interpretation
– In this output only question number 1 shows the age differences between 21-
23 to 24-27. Sig. value is less than or equal to .05 (e.g. .03, .001), there is a
significant difference somewhere among the mean scores on your dependent
variable for the three groups.
– In question number 1 the p value (sig.) is 0.022 (which is less than 0.05) so
there is a difference in the mean of the age group.
– Asterisks (*) means that the two groups being compared are significantly
different from one another at the p value is less than 0.05.
– In this output only age group 21-23 mean difference (i-J) is -0.529 and 24-27
mean difference (i-J) is 0.529 are statistically significantly different from one
another. That is, the 21–23 age group and the 24-27 age group differ
significantly in terms of their optimism scores.

22. Findings
- The present study suggests that one of the alternative hypothesis was rejected
where null hypothesis was accepted and the other alternative hypothesis was
slightly accepted.
–Hypothesis:
–Ha: There is an impact of impulse buying on student behavior on the basis
of
–gender. [Rejected]
–H0: There is no impact of impulse buying on student behavior on the basis
of
–gender. [Accepted]
–Ha: There is an impact of impulse buying on student behavior on the basis
of age
–group. [Rejected, slightly accepted]

23. Limitations
– The study has limitation in a way that it was
conducted from business students only so this
research can extend to other students overall
from other departments as well for more clarity
of results.

24. DEMOGRAPHICS
Consumer behavior of impulse
buying among students. Our
questionnaire was conducted
online through Google forms in
which there were total number of
62 respondents who participated
and gave their feedback from
whom 27 (43.5%) were male and
35 (58.5%) were female
respondents respectively.
•GENDER

25. •AGE
Age groups were classified into 4 sub-groups in which results were compiled as
following:
18 to 20 (28 respondents, 45.2%)
21 to 23 (31 respondents, 50%)
24 to 27 (5 respondents, 8.1%)
28 to 30 (1 respondent, 1.6%)
As shown in the picture that this is how we classified the age groups and results were
these from total number of 62 respondents.

26. IMPORTANCE OF
QUESTIONS
– We conducted 10 questions through which results were found
out that only two questions were creating an impact of
impulse buying on age groups and genders.
– Question 1: Did you buy any product without actually
planning for it?
– We concluded results of this question on the basis of one way
Anova because there were more than two age groups so the
results show that age groups of 21 to 23 and 24 to 27 buy
products without actually planning for it which shows that
there is an impact of impulse buying on these particular age
groups

27. Question 5: Do you think
price plays a vital role in
developing impulse buying
behavior?
– This question gain our interest because when we saw in the
SPSS viewer, sig value was less than 0.05 so we took interest
to find out that is this showing some significance effect on the
basis of gender. So according to SPSS guide if sig value is less
than 0.05 then we see the second line of sig (2 tailed values)
which is p value but there, we saw value of 0.83 which is
greater than 0.05 and then to test this further, we went on to
see 95% confidence interval upper and lower bound values of
the same second line but in between them there was the
presence of 0 because one value was positive and another one
was negative so we concluded no results from this question on
the basis of any difference on Gender.

28. Conclusion:
– Interpreted results on Descriptive statistics
– Went on to conduct T-test but it wasn’t substantial
– Unplanned buying occurs due to many factors so this was
basically the reason of taking this topic to analyze different
factors and to see the impact.

29. – But this study can be successful enough if it can be
generalized rather than to business students and this data
can prove effective if it is more properly conducted. But
this was a much more than a learning experience only.
Working on SPSS for the first time and concluding results
that way was a really good experience as a group and we
found out quite interesting results through collective
efforts.

30. Thank You 