A Comparison of the impact of advertising on Customer Buying Behavior Through Different Media

Marketing may influence consumers' behaviours and, in turn, the social fabric of a nation (Latif & Abideen, 2011). With effective categorization, the outcome and values associated with this group in memory are transferred to the target itself. Customers attempt to classify a brand's relationship with their present memories when faced with thousands of goods, which may reorient memories to a brand profile and perception of new products. As a result, people would be able to group current information into a certain category and organise it accordingly in their minds. Businesses that emphasise other goals above selling their products fare poorly in terms of retail sales. Others use a variety of advertising mediums to reach their target audience, including television, the internet (Facebook, email), newspapers, billboards, magazines, and so on. To sell their products and services, businesses spend a considerable percentage of their budgets on promotional efforts. This marketing strategies influence consumers' purchase choices (Abideen & Latiff, 2011). SakkthivelAM et al. (2015) In their investigation, they make an effort to determine if social networking websites have any impact on young women shoppers from Islamic nations. In order to examine the influence of young women customers, this research used structural equation modelling. The findings showed that brand, society, and reference teams had a greater impact on young women consumers' purchasing decisions via social media. Tayyaba Noreen and others (2015) Researchers looked at the effects of social media in Pakistan and Korea. This research looks at how social media affects customers' purchasing decisions. This research focuses on social media, online word-of-mouth, and publicity. According to the results, consumers in Korea are more likely than consumers in Pakistan to purchase a product using social media sites. The findings showed that, in comparison to social network advertising, e-word of mouth had a greater influence on purchasing intention. Balakrishnan et al. (2014) revealed that young people's brand impressions and purchases are influenced by their use of social media. We distributed 200 surveys to undergraduates in Malaysian institutions. Seventy-five percent of those who were asked did so. Three hypotheses and two propositions were tested using multivariate analysis and the mean. The results revealed that online communications, online communities, electronic word-of-mouth, and online publicity are helpful in increasing consumers' opinions of companies and their inclination to make purchases through social media platforms. These data notify managers that social media is the ideal sales approach for obtaining consumers from the younger generation. This research might be used by international retailers to promote their items through social media platforms in the United States. was already noted. However, there is also minimal descriptive and causal design in the research, which helps to make the study entire. Since any research must, by its very nature, employ all three of the methodologies, it is impossible for any study or research to be unique. The distinctions between these three categories are quite well defined. Every research project begins with descriptive methods before lurching into other, more advanced approaches like multivariate regression, GLM, factor analysis, cluster analysis, etc. At this point or level, the research seeks to test the hypothesis or any study assumptions in question and either refutes or supports the same. The exploratory techniques used in this study include factor analysis, which seeks to identify the study's unmeasured variables, sometimes referred to as dimensions or components. At this level, the research will actually be looking for specific, very logical, but significant notions in the data. The final responsibility of the researchers may then be to determine if the available data is really capable of assisting in the search for any evidence that supports the study hypotheses or not. Part 3 of the data analysis, section on Factor Analysis, has further details.

The study assumes that the gender differences to shopping behavior do exist. There are two tables one for frequencies which describes cross table. The other for summary statistics along with their P Values. The third is for linearity relationships. The data is ordinal hence normal Chi-Square test is not sufficient to investigate the relationship. The investigation might need few other tests such as Gamma, Spearman rho and Karl Pearson coefficients to cross check CMH test statistic.

are basically Male and they agree that their Shopping Behavior is not much influenced by family members. almost 230 of 592 female individuals agree that their Shopping Behavior is not influenced by ―Family‖. Interestingly there are 152 female individuals from those who agree that their shopping behavior influenced by family. The other interesting finding is that there are literally none of the male individuals who agree to this statement. This shows it clear that the male behavior is not influenced by the family members whereas female behavior is influence by the family members. The other interesting finding is that while 152 individuals agree but a majority i.e. 210 of female respondents disagree to the statement.

Table 2 shows the summary statistics for the cross tabulation between gender vs. Family Influence over Shopping Behavior. All statistics are significant for all the P Values are less than 0.05. So at 5 % significance level it might be possible to reject the null hypothesis that Family Influence over Shopping Behavior is independent of Gender. This means Gender doesn‘t influence the way survey respondents shop goods and it is completely free from any influence that might arises from their family.

Table 3 shows the symmetry measures for the above cross tabulation. From the table it is clear that the association between these two variables i.e. Gender and Family Influence found to be poor. All the values are feeble with their respective P Values.

• Gender * Buying After Considering Feedback Friends And Colleagues

―Buying after considering feedback friends and colleagues‖ is the second manifest variables for the very first factor (F1). The following tables shows Chi-Square analysis for this relationships.

From the table it is clear that out of 960 survey respondents maximum of female individuals i.e. 262 of them seem to depend on friends and colleague‘s recommendations. This is opposite to male respondents a maximum of 225 out of 368 of them seem to depend on friends and colleagues. However, going by category maximum of 405 individuals who seem to depend has both male and female respondents and most of them in this category appears to be female respondents.

Value Df Asymp. Sig. (2-sided) Pearson Chi-Square 237.591a 2 0 Likelihood Ratio 306.326 2 0 N of Valid Cases 960 The Table 6 shows the Chi-Square tests for the above mentioned relationship. All the P Values are less than 0.05. So at 5 % significance level it might not be possible to accept null hypothesis. This means that family and colleagues respondents influence over buying behavior pretty much depends on Gender. All the relationships are significant.

Table 7 shows the linear relationship between Gender vs. Friends and Colleagues Influence. All the relationships are poor or week with their P Values being less than 0.05. This means that though the relationship is weak in the sample but the same in the population is significantly different from zero. This makes it possible to state that the real relationships can be other than zero but weak. Figure 4.13 adds visualization to the above analysis.

This is third variables of the very first factor (F1). The study assumes that the influence of Location and Cultural Background of study respondents pretty much depends on Gender. The table 8 shows the particulars of cross tabulation between above mentioned variables.

SMEAN(Loc_Cultural background_Affects_Purchasing) Total 2 3 5 Gender Male 143 225 0 368 Female 152 230 210 592 Total 295 455 210 960 225 out of 368 of male respondents are neutral for Influence of Location and Cultural Background. Few of them i.e. 143 do disagree that the influence exists. Interestingly there are zero individuals who strongly disagree. However, contrary to this there exists 210 female individuals who Strongly Agree to the fact that their Shopping Behavior pretty much influenced by their Location and Cultural Background. Regardless o this fact, a maximum of i.e. 230 out of 592 female respondents are found to be neutral to this statement. Going by the category a maximum of 455 individuals comprising both male and female out of 360 appears to be neutral

Table 9 shows the Chi-Square test for above mentioned relationship. all sample statistics appears to be significant at 5 % significance level. So it is not possible for the study to accept null hypothesis. So, the Influence of Location and Cultural Background significantly influenced by Gender.

Table 10 shows the cross tabulation for the last variable of the very first factor i.e. F1. The table shows the frequencies for cross relationship between ―Influence of Social Norms over Shopping Behavior‖ and its dependence over Gender. but found mostly neutral (472). Going by category, a maximum of 615 out of 960 appears to be neutral. This means that the respondents are not clear that whether their shopping behavior really influenced by social norm or not.

Table 10 shows Chi-Square Statistics for the above mentioned relationship. All the statistics are statistically significant. So the study can reject the null hypothesis that there exists Gender level differences for with respect to Influence of Social Norms over their Shopping Behavior.

Table 11 shows statistics for linear by linear relationships. The relationships don‘t really seems to be linear for all the values are weak but significant. All the P Values are less than 0.05. so at 5 % significant level the relationships in the sample are suspicious. Figure 5.15 adds visualization for the above description.

The purpose of the factor analysis is to reduce the dimensionality of data and see if it is possible to explain any hidden variables. The study has 128 variables explaining 29 factors with two constructs. The constructs are (1) Socioeconomic Profile, (2) Consumer Behavior. Socioeconomic profile is explained through seven variables such as Gender, Age, Education, Marital Status, Residence, Occupation and Monthly Income. The second construct has 29 factors composed of 128 variables. The ultimate purpose of factor analysis to excavate evidence in support of the study. The study assumes that these are several factors which can be explained through the sampled data. The following table shows the Eigen values obtained through factor analysis using principal components analysis. Eigen values helps in evaluating the variables whether they represents any underlying dimension which is not only general but unique in the data. From the table it is clear that all the variables in the data has positive values. This is again an auspicious symptom in the study. Moreover the Karl Pearson Correlation coefficient for Initial and Extraction components appears to be -0.01039. This shows that there exists zero correlation between before and after extraction, which means the extraction really helps the study in explaining the factors.

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Research Scholar, Shri Krishna University, Chhatarpur M.P.