Research Problem

The impact of online offers wooing young customers.

Objectives of the Study

This project is conducted for achieving the following objectives.

1. To study the customer attitude towards offer sales.

2. To identify the influence of offer sales in buying behaviour.

3. To measure the offer proneness of young customer.

4. To measure the consumer awareness for different types of online offers.

Research Methodology

Research methodology is way to systematically solve the research problem. It is a plan for action for a research project and explains in detail how data are collected and analyzed research methodology may be understood as a science of studying how research is done scientifically. It can over wide range of studies from simple description and investigation to be construction of sophisticated experiment.

Research Period

The study was conducted for a period of 2 months.

Source of Data

We have collected data from primary sources alone.

Primary Data

We have collected our primary data through questionnaire. We have distributed 200 questionnaires out of which we could identify only 120 respondents.

Sample Size

Sample size is an important feature of any empirical study in which the goals is to make inferences about a population from a sample. In practice, the sample size used in a study is determined based on the expenses of data collection and need to have sufficient statistical power. A group of 120 respondents were selected for this study. Alina (2014), Bharadwaj, (2007); Brennan, (2003); Cui, et al. (2012).

Sampling Method

The respondents are online customers. There are millions of online customers spread around various parts of the world. Eventhough,we can’t establish that their behaviour pattern and nature are alike, we opted convenience sampling mostly because of the limited period of study, expenses of data collection and scope of accessibility of respondents Delafrooz, et al. (2010).

Method of Data Collection

Questionnaire is used for data collection. We have distributed 200 questionnaires out of which we could identify only 120 respondents respondents Diehl (2001); Kim, et al. (2007); Lee & Lin (2005); Lewis (2004); Lim, et al. (2016).

Tools Used For Data Analysis

1. Correlation

Correlation is a bivariate analysis that measures the strengths of an association between 2 variables and the direction of the relationship. In terms of the strength of relationship,9the value of the correlation coefficient varies between +1 and -1.

2. ANOVA

Analysis of variance testthe hypothesis that the means of two or more populations are equal. ANOVA assess the importance of one or more factors by comparing the response variable means at the different factor levels. The hypothesis states that all population means are equal while the alternative hypothesis states that at least one is different.

3. T-test

A T-test is a statistical examination of two population +means. A two sample T-test examines whether two samples are different and is commonly used when the variances of two normal distributions are unknown and when experiment uses a small sample size.

4.Chi-square test

Chi-square test is a statistical method assessing the goodness of fit between a set of observed values and those expected theoretically.

Data Analysis and Interpretation

Testing of hypotheses

Following are the statistical tool used to fulfill the hypothesis.

A.Chi-square test

B.T-test

C.ANOVA

D.Correlation.

A.Chi-square test

H1: Gender and perception of internet advertisement are independent.

H2: There is no association between gender and online shopping satisfaction of respondents.

B.T-test

H3: There is no significant difference between male and female in their offer proneness.

H4: There is no significant difference between married and non- married in their offer proneness.

C. Anova

H5: There is no significant difference between consumer having different levels of age in their offer proneness.

H6: There is no significant difference between consumers having different level of education in their offer proneness.

D. Correalation

Hypothesis 7: There is no correlation between buying behaviour and attitude of respondents.

H8: There is no correlation between offer proneness and buying behaviour of respondent.

Testing of Hypotheses

Chi-square test: Chi-square of independent and results are applied to test the hypothesis given below.

H1: Gender and perception of internet advertisement are independent.

Interpretation

The Pearson chi-square value is 3.604 with 4 degree of freedom and p value is 0.462>0.05 which is not significant significant Rezaei, & Amin (2013); Rohm (2004) & Swaminathan, (2004). So the hypothesis gender and perception of internet advertisement are independent is accepted Tables 1-4.

Table 1 Gender *Perception of Internet Advertisement Cross Tabulation
Gender	Never	Rarely	Sometimes	Often	Always	Total
Male	1	13	26	21	12	73
Female	1	4	16	13	13	47
Total	2	17	42	34	25	120

Table 2 Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	3.604	4	0.462

Table 3 Online_Shopping * Gender Crosstabulation
Gender	Highly Dissatified	Dissatified	Neutral	Satified	Highly Satified	Total
Male	0	1	6	55	11	73
Female	0	0	4	31	12	47
Total	0	1	10	86	23	120

Table 4 Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	2.631a	3	0.452

H2: There is no association between gender and online shopping satisfaction of respondents.

Interpretation

There Pearson Chi-square value is 2.631 with 3 degree of freedom and p value is 0.452 is greater than 0.05 which is not significant. So the hypothesis there is no association between online shopping satisfaction and gender and the hypothesis is accepted in Tables 5-8.

Table 5 Group Statistics
Gender	N	Mean	Std. Deviation	Std. Error Mean
male	73	49.9452	5.87578	0.68771
female	47	50.2340	6.38345	0.93112

Table 6 Independent Sample Test
	t-test for Equality of means
	t	df	Sig. (2-tailed)
Offer proneness	-0.254	118	0.800

Table 7 Group Statistics
Offer proneness	Maritial_status	N	Mean	Std. Deviation
	married	19	48.2632	6.05385
	non-married	101	50.3960	6.02508

Table 8 Independent Sample Test
	t	df	Sig. (2-tailed)
Offer Proneness	-1.415	118	0.160

T-test

Independent sample T-test is applied to test the hypothesis given below the results are:

H3: There is no significant difference between male and female in their offer proneness.

Interpretation

The t-test shows t value is -0.254 with degree of freedom 118 and the significance value of t is 0.800 greater than 0.05.Therefore it is accepted that there is no significant difference between male and female in their offer proneness Schuster & Sporn (1998); Thananuraksakul (2007); Vollrath, et al. (1998); Wen & Huang (2006); Xu & Huang (2014); Zhou, et al. (2007); Zhu & Zhang (2010). The mean difference value also shows that there is no difference.

H4: There is no significant difference between married and non- married in their offer proneness.

Interpretation

T-test shows t value is -1.415 with degree of freedom is 118 and the significant value of t is 0.160 greater than 0.05.Therefore it is accepted that there is no significant difference between married and non-married in their offer proneness. The mean difference value also shows that there is no difference in Tables 9 & 10.

Table 9 Annova
Levels Of Age	Mean Score
	N	Mean	Std. Deviation
18-21	70	50.4000	5.94516
21-24	31	50.7097	6.81759
24-27	15	46.9333	4.60538
27-31	4	50.7500	4.57347
Total	120	50.0583	6.05479

Table 10 Anova Test
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	169.721	3	56.574	1.565	.202
Within Groups	4192.870	116	36.145
Total	4362.592	119

Anova

The statistical tool ANOVA is used to test the hypothesis given below and the results.

H5: There is no significant difference between consumer having different levels of age in their offer proneness.

Interpretation

The statistical tool used ANOVA shows the f value is 1.565 and the significant value 0.542>0.05.Therefore the hypothesis is accepted. So it can be concluded that there is no significant difference between consumers having different levels of age in their offer proneness. Highest mean is in the age 21-27, mean age is 50.7097 in Tables 11 & 12.

Table 11 Level of Education
Level of Education	Mean Score
	N	Mean	Std. Deviation
SSLC	1	56.0000
PLUS WO	27	49.7407	5.80843
DEGREE	79	50.0506	6.44284
PG	8	48.8750	4.35685
DIPLOMA	5	52.6000	2.88097
Total	120	50.0583	6.05479

Table 12 Anova Sum
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	81.534	4	20.384	0.548	0.701
Within Groups	4281.058	115	37.227
Total	4362.592	119

H6 There is no significant difference between consumers having different level of education in their offer proneness.

Interpretation

The statistical tool used ANOVA shows the f value is 0.548 and the significant value 0.701>0.05. Therefore the hypothesis is accepted that there is no significant difference between consumers having different level of education in their offer proneness. Highest mean in SSLC and the mean are 56.000. Correlations used to test hypothesis given below and the result are Table 13.

Table 13 Correlations
		BUYING_BEHAVIOUR	ATTITUDE
BUYING_BEHAVIOUR	Pearson Correlation	1	0.214*
	Sig. (2-tailed)		0.019
	N	120	120
ATTITUDE	Pearson Correlation	0.214*	1
	Sig. (2-tailed)	0.019
	N	120	120

H7: There is no correlation between buying behaviour and attitude of repondents.

Interpretation

The p value is 0.019<0.05.The calculated value is less than able value, so the hypothesis is rejected. So there is correlation between the buying behaviour and attitude of respondents in Table 14.

Table 14 Correlations Behaviour
		OFFER_PRONENESS	BUYING_BEHAVIOUR
OFFER_PRONENESS	Pearson Correlation	1	0.277**
	Sig. (2-tailed)		0.002
	N	120	120
BUYING_BEHAVIOUR	Pearson Correlation	0.277**	1
	Sig. (2-tailed)	0.002
	N	120	120

H8: There is no correlation between offer proneness and buying behaviour of respondents.

Interpretation

The p value is 0.002<0.05.The calculated value is less than the expected value, so the hypothesis is rejected. So there is correlation between the offer proneness and buying behaviour of respondents.

Findings of the Study

1. In this study, the researcher find that majority of respondents are more aware about the online shopping site flipkart than other online shopping sites and they prefer flipkart for their online shopping.

2. Flipkart and Amazon are the important online shopping sites.

3. This study also finds that, product quality is the most important factor considered by the respondents in their online shopping.

4. .Majority of respondents are more aware about normal offer.

5. Other important offers are Cash on Delivery and Free Shipping.

6. It also significantly notes the offer proneness of respondents.

7. Gender and perception of internet advertisement are independent

8. There is no association between online shopping satisfaction and gender.

9. There is no significant difference between male and female in their offer proneness.

10. There is no significant difference between married and unmarried in their offer proneness

11. There is no significant difference between consumer having different levels of age in their offer proneness.

12. There is no significant difference between consumer having different levels of education in their offer proneness.

13. There is correlation between buying behaviour and attitude of respondents.

14.This study also finds that, there is correlation between buying behaviour and offer proneness of respondents.

Implications of the Study

1. In this research, the researcher tried to find out the relationship between various types of online offers and buying behaviour of consumers.

2. The findings of the research can open a new insight to online suppliers, so that they can segment their market,more carefully focus mostly on product quality.

3. The study also implies that the online suppliers must understand the attitude of the online customers and then build the product according to customer preference.

4. The study also implies that advertisements, promotions and other marketing activities can be developed according to the customer preference.

5. It also implies that the online shopping is increasingly done by young customers and the manufacturer has to produce the product according to their interest.

Recommendations of the Study

1. The online suppliers should segment their market, more carefully focus mostly on product quality, preference and attitude of the different type of online offers.

2. The online supplier must understand the attitude of the customersabout what type of offer they prefer and then frame the offers according to the preference.

3. Young customers are increasingly buying online, so suppliers must consider the tastes of the young customers.

4. Product manager should focus on product quality because product quality is increasingly impacting the buying behaviour of the customers.

Limitations of Study

1. Poor co-operation from some respondents.

2.Lack of diversity in terms of demographic characteristics likes marital status, age because the sample population was young customers.

3.The sample had a limited size, because the research was carried out in a local area within a period of 2 months.

Conclusion

The project entitiles “Impact of online offers wooing young customers”. This study examined that whether there is a relationship between buying behaviour and offer proneness of respondents. The findings of the study supported the hypothesis that there is no relationship between gender and their offer proneness. So this research study concluded that majority of consumers are satisfied in their online shopping. In the study majority of respondents are more aware about normal offers and other important offers are cash on delivery and free shipping. In the present scenario,nobody is interested in spending time for buying households goods and other essentials. Everyone is trying to figure out how to buy products of good quality with minimum time. So we decided to study about the online sites that people generally use to buy. This study has enabled us to understand the types of offers that online sites provide to the public and the relationship between offers and buying behaviour of people. We had a hard time in collecting the right information from the respondents. They were less willing to cooperate with us. Through this study, we were able to study the different types of online offers. We studied the relationship between online offers and customers’ buying behaviour. We found this as a great opportunity to learn about online offers.

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