Factors Affecting Purchase Intention

1.1 BACKGROUND OF THE STUDY

Ecommerce sector in India is considered to be the sector where huge developments and growth opportunities are lying. Many research firms across the world have forecasted that this sector which is about $3 billion in 2013 will grow to $22 billion in the next five years. Most of the companies are trying to increase their sales in most innovative ways besides using various online promotional tools like improving payment options and security issues.

As a frequent online shopper on various websites every online user always eagerly wait for exciting discounts, coupons or other promotions on the websites that puts me into a queue and ends me up in buying something or the other at that point of time. So for companies in order to achieve the sales goals, it's important for them to design and implement most attractive sales promotions. It's also important that before making any sales promotion is should be financially analyzed like what is the loss that a promotion could bring or what is the potential for the return on investment. I have observed that the promotions go on at various seasons in most attractive ways and I also find that companies keep on changing their prices of products in the course of putting them in to these promotional tools which is quite difficult and also should be done with utmost care.

As it is difficult for companies to know which tool is performing best in the industry as they could get information only about their own customers, So knowing which online promotional tool is impacting the purchase intention of the customer is of great importance for all online retailers. This is what exactly this research paper wants to analyze

1.2 NEED FOR THE STUDY
In our day to day life we would be seeing number of mails or number of places where online sales promotions are hitting on the screens in the course of motivating every online user to buy something or the other through online.

We use to see sales promotions going on weekends, but these days we could see promotions going on almost every day. The main reason is to ease users buy things online whenever they plan of buying something. So companies are creating situations in different ways to make it happen in the easiest way for the online customers. In the curse of these they are spending a bomb on sales promotions that is hampering their profits which is very important for this sector as most of the companies are funded by venture capitalists where they seek immediate returns. Therefore, knowing on which promotional tool to spend is very important for the companies to attract customers easily and hence increase their sales. One good thing about this industry is the number of online users is increasing at a much faster pace compared to even biggest markets like china and South Korea. India even though the online users are very huge ecommerce industry is considered to be in nascent stage only. As a course all this support is given to the industry in various ways in terms of funding and also government regulations in terms of carrying business. We could even see third party sites like couponation.com, Smartsource.com etc that are helping many online retailers to increase their sales through their business model of sourcing coupons to customers to buy on various platforms.

So companies should definitely know the importance of which tool is effecting the purchase intention of the customer and then act or plan their promotional strategies accordingly rather than going on promotions with every tool without considering the affect that it has on the return on investment could be devastating for the companies in this industry

1.3 PURPOSE OF THE STUDY
The purpose of this study is that it gives a brief picture of which promotional tool is doing better so that it helps companies to better plan on which promotional tool they have to concentrate. The companies in this industry might know which promotional tool is doing well with respect to their own customer base, but they couldn't find easily which is doing well in the industry. So this study gives an insight of which promotional tool is leading to purchase intention which ultimately leads to increase in sales especially in the online apparel industry.

2.1 INTRODUCTION
Purchase Intention has become more complex and more important for the consumers in present day world. Customers are being bounced with lot of information through direct mailings, Advertisements, and several other sources. Importantly because of advent of multiple sources of purchasing like malls, super bazaars, online portals etc the decision making had become more difficult for customer. This study mainly deals in knowing the purchase intention of the people who are buying apparel online in various portals. There are several studies conducted especially in brick and mortar model of retail that have proved that sales promotional tools have significant impact on purchase intention of the customer. Even promotion of the sales promotional tools like coupons, rebates, price discounts also have significant impact on both purchase intention (Rizwan, M. (2009). 'Affect of Sales Promotion Tools on Purchase Intention of Consumer') and also on the perceived switching cost. All online retailers are seeing this as a very important thing as the benefits that are being provided by the original provider will be decided based on number of customers who are buying on your online portal. So, if people are switching to other players especially in this industry the switching cost is very low that it is just a matter of a click. So, all retailers have considered this as a very critical aspect of doing their business.

E-commerce industry has been one of very few booming industries around the world. Many Companies under this industry have grown to peaks and because of the barrier to entry is very low huge competition has been build which slowly turned into an unhealthy competition and many companies went into Collaborations that had happened in the recent past because of the immediate profits expecting from the Venture capitalists that have funded the companies. E-commerce companies have found Sales promotions as one of the best ways to persuade customers to purchase products. As we can see E-commerce companies are doing Various Sales Promotion to increase their sales by framing their promotional offers in most innovative ways that might end up the customer in Buying. So my focus is on understanding the influence of these Sales Promotions that the companies in this industry coming up with by spending lot of time and money to make the Apparel shoppers to prefer online purchase from their online portal.

2.2 REVIEW OF LITERATURE
For the purpose of this Research various past studies have been reviewed. Sales Promotion has been considered as the most stimulating technique of promotion for influencing the purchase intention. It is a valuable tool for both manufacturers and retailers. As per the survey of Cox direct (1998) on promotional practices suggest that many companies spend as much as 75% on Sales promotion and25% on Advertising of their total promotional Budget. Shih-fen s.chen, k. B.-c. (1998) said that Promotional Framing has a significant impact based on the Involvement level of the consumers.The study revealed that Product price, Discount promotions has a significant impact on purchase intention. A study done by Harimukthi, A. K. (2012, Oct) revealed that product design and features of the store was not able to make any comment on the impact of the promotions. In a study conducted by Rizwan, M. (2009) revealed that people with more promotional Knowledge use coupons more and also revealed that there is a positive effect of price discount on Purchase Intention. It came to know that there is no relationship between purchase intention and free sample. The same study helps us to know that social surroundings has a significant impact on the purchase intention of the customer.
In a Study conducted by Hayan Dib, M. A. in 2013, Sep to know the impact of sales promotion on percieved transaction value and purchase intentions revealed that Price discounts are more effective tham premiums that generate higher buyer intentions if the promotional benefit is high and if the promotional benefit is low then price discounts are more effective than premiums. In another study conducted by Soni Neha, V. M. (2013, July) revealed that among various promitonal tools offer, premium and contest are the most influencing variables of consumer purchase decision. Price pack and rebate are seemed to be insignificant in this research. The perception of the customer towards the pricing of a product may also have an impact on his/her purchase decision so on these lines to know whether than has an impact or not a study conducted by Teresa Montaner, L. d. (2011) revealed that odd pricing products high percieved acquisition value than even pricing and odd prices leads to higher purchase intention. The same study also revelaed that Promotional labels produce high percieved value higher brand awareness produces higher purchase intention.

In a study conducted by Patrali Chaterjee in 2007 on 'Advertised versus unexpected next purchase Coupons: Consumer Satisfaction, Perception of value and fairness' revealed that the unexpected purchase coupons leads to higher purchase satisfaction but lower perceptions comapred to advertised coupons whereas Strong purchase satisfaction where there are unexpected and unrestricted start date coupons. Companies spend huge amounts of money on Coupons and make their Sales strategy by making sure that the face value of the coupons doesn't cost more than they are worth in increased sales. On the similar lines a study was conducted in 2005 by Barat, Somjit; Paswan, Audhesh K that revealed that for low face value coupons the intention to redeem is positively associated with face value and for higher face values of the intention is more or less unchanged. The study also revealed that the intention to redeem the coupon and the percieved sticker price of the product is positive at the lower levels of the coupons face value but becomes negative at higher face values

Figure 2.1: Factors affecting purchase intention

2.3 HOW REVIEW HAS BEEN CONDUCTED
This review is conducted by first collecting various research journals on topics related to sales promotional tools and purchase intention. Around 15 research articles were collected where the research was conducted in various parts of the world. These articles were reviewed and then some research gaps were found that gave me an insight to take a research on this concerned topic which is considered to be one of the hot topics of ecommerce industry in India as companies are not breaking even and spending huge amounts of money on sales promotions to attract customers.
So after the review of various literatures on the bases on the knowledge I gained I have decided to conduct the research on 'Influence of online sales promotional tools on purchase intention of the customer in the apparel industry'

3.1 PROBLEM DEFINITION
Problem definition is done in three steps mainly

Figure 3.1: Steps in problem statement


3.1.1 MANAGEMENT DECISION PROBLEM

'Will too much spending on sales promotions for the companies turn out to be bad decision for the company in the near future as it might impact their profits and return on investments'?

Ecommerce companies are in infancy stage as of now. They are seeing tremendous increase in their sales as the number of online users were being added day by day, In India the online users for the year 2012-2013 has jumped up by 47% compared to 8% -10% in china and other emerging economies. So for ecommerce companies time has come to take this as a advantage and grow faster. At the same time companies are spending a bomb on their promotional campaigns to attract customers which is affecting their returns and margins and at the end of the day their profits. Too much spending on sales promotions might also create negative vibes of converting users into customers

3.1.2 MARKETING RESEARCH PROBLEM
'To find which online sales promotional tool is impacting the purchase intention of the customer more in the apparel industry.'

All companies are in a hunting spree for customers and hence they are even ready to spend huge amounts to attract them through various ways of promotions. Making use of every sales promotional tool is always a costly thing without knowing which one is doing well and how innovatively we can attract customers by making use of it. Distribution of coupons, vouchers is not that difficult in ecommerce compared to brick and mortar but definitely if not planned properly which distribution channel to be used it would turn to be like shooting in the dark blind folded.

3.1.3 PROBLEM STATEMENT
' The overuse of sales promotion activities have the potential of resulting in less positive attitude towards the product
' Sales promotions require very high implementation costs
' Profitability comes down when sales promotions are carried out for a longer period of time
3.2 OBJECTIVE OF THE STUDY
Primary Objective
' The primary aim of this study is to analyse and find which online promotional tool is influencing the purchase intention of the customer in the apparel industry
Secondary Objective
' The study also aims to check if there is any significant difference in purchase intention across Income groups, occupation and levels of adopters

3.3 RESEARCH QUESTIONS
There are five main research questions used by the researcher to guide the creation of the study
Hypothesis 1(H1):
Attitude towards coupons has a significant impact on the purchase intention of the customer

Hypothesis 2(H2):
Attitude towards product bundling has a significant impact on the purchase intention of the customer

Hypothesis 3(H3):
Attitude towards price discounts has a significant impact on the purchase intention of the customer

Hypothesis 4(H4):
Attitude towards rebates on the purchase intention of the customer

Hypothesis 5(H5):
Attitude towards point of purchase displays on the purchase intention of the customer

Hypothesis 6(H6):
Is the purchase intention same across income groups

3.4 SCOPE
' The present study can be extended to other geographical areas.
' It can be extended to study various other factors that are affecting customer purchase intention
' This study can be extended to understand the switching behaviour of an online user from one site to other site

3.5 RESEARCH METHODOLOGY
' This study follows a quantitative, descriptive approach. It involves studying the customer's mindset about the online sales promotional tools and their intention towards going for particular promotional tool during their purchase
' The main aim of this study is to find out if there is any influence of online sales promotional tools on the purchase intention of the customer in the apparel industry


Figure 3.2: Research Methodology
3.5.1 PRELIMINARY INVESTIGATION
This phase involved collection of the various factors that could affect the customer's purchase intention. The preliminary investigation was done accordingly

3.5.1.1 SECONDARY DATA ANALYSIS
The secondary data available was reviewed to narrow in on a list of potential factors that might affect a customer's purchase intention. The various media of promotions that the companies are using were also listed down in an attempt to understand their influence on a customer purchase intention

3.5.2 RESEARCH APPROACH
1. The factors for purchase intention in the ecommerce industry can be obtained from the literature review.
2. The primary data would be collected by administrating questionnaire.

3.5.3 SAMPLING PROCESS
3.5.3.1TARGET POPULATION:
Online shoppers (Apparel websites)
o Students
o Working Professionals

3.5.3.2 SAMPLING FRAME
' Online Apparel shoppers in the Bangalore city

3.5.3.3 SAMPLING ELEMENT
The respondents would be online shoppers who shop for apparel

3.5.3.4 SAMPLING METHOD
Stratified random sampling will be adopted because this study considered members from different websites that sells apparels

3.5.3.5 SAMPLE SIZE
As seen in most of the literature Reviews that the sample size is between 150 and 300 and hence it's been decided to go with a sample size of 200 which is average of both limits of samples that are observed in literature review

3.5.3.6 ADMINISTRATION:
The data has been collected through personal administration of questionnaires and online administration of questionnaires.

4.1 PROFILE OF THE SAMPLE
OCCUPATION
The Respondents exhibited the following pattern in terms of their occupation. The 5 main categories included were namely
1. Student
2. Working professional

Table 4.1:Respondent profile by Occupation
Frequency Percent Valid Percent Cumulative Percent
student 123 59.7 59.7 59.7
professional 83 40.3 40.3 100.0
Total 206 100.0 100.0


Figure 4.1: Pie-chart showing the occupation pattern



INCOME
The Respondents exhibited the following pattern in terms of their Monthly income level. The sample has been categorized into 4 namely
1) less than Rs.30,000 2) Rs.30,000 to Rs.50,000 3) Rs.50,000 to Rs.70,000
4) more than Rs.70,000

Table 4.2: Respondents profile by Monthly Income
Frequency Percent Valid Percent Cumulative Percent
less than 30000 99 48.1 48.1 48.1
30000-50000 55 26.7 26.7 74.8
50000-70000 21 10.2 10.2 85.0
more than 70000 31 15.0 15.0 100.0
Total 206 100.0 100.0

Figure 4.1: Pie-chart showing the Income pattern

LAST SHOPPED ONLINE:
The Respondents exhibited the following pattern in terms of their latest online purchase.
The sample has been categorized into 4 namely
1) < 3 months 2) 3-6 months
3) 6-12 months 4) More than 1 year

Table 4.3:Respondents based on their last shopped apparel online
Frequency Percent Valid Percent Cumulative Percent
< 3 months 116 56.3 56.3 56.3
3-6 months 52 25.2 25.2 81.6
6-12 months 12 5.8 5.8 87.4
> than 1 year 26 12.6 12.6 100.0
Total 206 100.0 100.0



Figure 4.1: Pie-chart showing the last purchase pattern
AGE
The Respondents exhibited the following pattern in terms of their age. The sample has been categorized into 4 namely
1) <20 years 2) 21-30 years 3) 31-40 years 4) Above 40 years

Table 4.4: Respondents profile by Age
Frequency Percent Valid Percent Cumulative Percent
<20 8 3.9 3.9 3.9
21-30 174 84.5 84.5 88.3
31-40 20 9.7 9.7 98.1
Above 40 4 1.9 1.9 100.0
Total 206 100.0 100.0


Figure 4.4: pie-chart showing age pattern of respondents
PLACE OF PURCHASE
The Respondents exhibited the following pattern in terms of where they shopped/ willing to shop. The sample has been categorized into 5 namely
1) Myntra 2) Jabong 3) Flipkart 4)Yepme 5) zovi 6) others

Table 4.5 Respondents based on where they shopped/willing to shop?
Frequency Percent Valid Percent Cumulative Percent
Myntra 63 30.6 30.6 30.6
Jabong 20 9.7 9.7 40.3
Flipkart 92 44.7 44.7 85.0
Yepme 11 5.3 5.3 90.3
Zovi 7 3.4 3.4 93.7
others 13 6.3 6.3 100.0
Total 206 100.0 100.0



Figure 4.5: pie-chart showing place of purchase of respondents
4.2 TESTING RESEARCH QUESTIONS
4.2.1 TESTING RESEARCH QUESTION 1
Q: Attitude towards coupons has a significant impact on the purchase intention of the customer
Null hypothesis Ho:
Attitude towards coupons does not have significant effect on purchase intention (??=0)

Alternate hypothesis H1:
Attitude towards coupons has a significant effect on purchase intention (?? '0)

Linear regression analysis is performed by taking purchase intention as dependent variable and Attitude towards coupons, Attitude towards product bundling, Attitude towards price discounts, Attitude towards Point of purchase displays and Attitude towards rebates as predictor (independent) variables.

The conceptual form of the model is
Purchase Intention = ??0 + ??1 (Attitude towards coupons) + ??2 (Attitude towards product bundling) + ??3 (Attitude towards price discounts) + ??4 (Attitude towards Point of purchase displays) + ??5 (Attitude towards rebates) + ??, where ??i is the regression coefficient and ?? is the random disturbance term assumed to follow normal distribution with mean zero and constant variance ??2.

Results of the regression analysis reported the fitted regression model as:
Purchase Intention = 1.379 ' 0.166 (Attitude towards coupons) ' 0.092 (Attitude towards product bundling) + 0.142 (Attitude towards price discounts) + 0.105 (Attitude towards Point of purchase displays) + 0.095 (Attitude towards rebates)

Coefficient of determination R2 = 0.346. This means that 34.6% of the total variation in preference in fitted (explained) by five independent variables. Result of the ANOVA F test is reported in Table 4.6. Results clearly indicate that the overall fitted regression model is statistically significant (F (1, 204) = 19.344, p =<.001). Results of the t test are reported in Table 4.6

Estimated regression coefficient for Attitude towards coupons is ?? = 0.166. Result of the t test indicates that the null hypothesis of no significant effect (Ho: ?? = 0) can be rejected at 0.05 level of significance (t (204) = 2.504, p = .013). This means that attitude towards coupons, controlling for the effect of attitude towards POP displays, Attitude towards rebates, Attitude towards price discounts and Attitude towards product bundling, does exhibit a significant effect on purchase intention. The study hypothesis H1 is supported.

Table 4.6 ANOVA Test results for regression Analysis with Purchase Intention as Dependent variable

Model Sum of Squares Df Mean Square F Sig.
Regression 28.474 5 5.695 21.208 .000b
Residual 53.703 200 .269
Total 82.177 205


Table 4.7: Estimated model parameters and results of t test for their significance


To test the seriousness of the problem of Multi-collinearity, tolerance level and variance inflation factor values are computed. None of the independent variables reported high VIF value. In fact, none of the independent variables reported VIF more than 2.0. Therefore the regression analysis does not suffer the problem of multi-Collinearity. There is no problem of inflation of standard errors and as such there is no serious multi-collinearity problem.

Residual analysis is performed to validate important assumptions of regression analysis including normality of residuals and constant error variance (homoscedasticity) of residuals. Descriptive statistics of residuals of the regression analysis is reported in Table 4.6. All the residual values lie within the range of -4 to + 4 indicating that there are no abnormally high or low residuals. Histogram of the residuals indicates no heavy departure from normality.
Residual plot of standardized predicated value against standardized residual values is reported in Figure 4.8. Residual plot indicates a very healthy plot with no specific pattern of residuals (random scatter of residual values). This means that the assumption of linearity, normality and constant error variance are satisfied.

Table 4.8:Descriptive statistics for Residuals
Minimum Maximum Mean Std. Deviation N
Predicted Value 1.9791 4.3801 3.4175 .37269 206
Residual -1.95706 1.49564 .00000 .51183 206
Std. Predicted Value -3.860 2.583 .000 1.000 206
Std. Residual -3.777 2.886 .000 .988 206


Figure 4.6 Histogram of residuals


Fig4.5 Residual scatter plot of standardized predicted and standardized residual values



'
4.2.2 TESTING RESEARCH QUESTION 2
Q: Is the attitude towards product bundling has an influence on the purchase intention?

Null hypothesis Ho:
Attitude towards product bundling does not have significant effect on purchase intention (??=0)
Alternate hypothesis H1:
Attitude towards product bundling has a significant effect on purchase intention (?? '0)

Linear regression analysis is performed by taking purchase intention as dependent variable and Attitude towards coupons, Attitude towards product bundling, Attitude towards price discounts, Attitude towards Point of purchase displays and Attitude towards rebates as predictor (independent) variables.
The conceptual form of the model is
Purchase Intention = ??0 + ??1 (Attitude towards coupons) + ??2 (Attitude towards product bundling) + ??3 (Attitude towards price discounts) + ??4 (Attitude towards Point of purchase displays) + ??5 (Attitude towards rebates) + ??, where ??i is the regression coefficient and ?? is random disturbance term assumed to follow normal distribution with mean zero and constant variance ??2.

Results of the regression analysis reported the fitted regression model as:
Purchase Intention = 1.379 ' 0.166 (Attitude towards coupons) ' 0.092 (Attitude towards product bundling) + 0.142 (Attitude towards price discounts) + 0.105 (Attitude towards Point of purchase displays) + 0.095 (Attitude towards rebates)

Coefficient of determination R2 = 0.346. This means that 34.6% of the total variation in preference in fitted (explained) by five independent variables. Result of the ANOVA F test is reported in Table 4.4. Results clearly indicate that the overall fitted regression model is statistically significant (F (1, 204) = 19.344, p =<.001). Results of the t test are reported in Table 4.5

Estimated regression coefficient for Attitude towards product bundling is ?? = 0.092. Result of the t test indicates that the null hypothesis of no significant effect (Ho: ?? = 0) cannot be rejected at 0.05 level of significance (t (204) = 1.497, p = .136). This means that attitude towards product bundling, controlling for the effect of attitude towards POP displays, Attitude towards rebates, Attitude towards price discounts and Attitude towards product bundling, does not exhibit a significant effect on purchase intention. The study hypothesis H1 is supported.

Table 4.4 ANOVA Test results for regression Analysis with Purchase Intention as Dependent variable

Model Sum of Squares Df Mean Square F Sig.
Regression 28.474 5 5.695 21.208 .000b
Residual 53.703 200 .269
Total 82.177 205

Table 4.7: Estimated model parameters and results of t test for their significance

To test the seriousness of the problem of Multi-collinearity, tolerance level and variance inflation factor values are computed. None of the independent variables reported high VIF value. In fact, none of the independent variables reported VIF more than 2.0. Therefore the regression analysis does not suffer the problem of multi-Collinearity. There is no problem of inflation of standard errors and as such there is no serious multi-collinearity problem.

Residual analysis is performed to validate important assumptions of regression analysis including normality of residuals and constant error variance (homoscedasticity) of residuals. Descriptive statistics of residuals of the regression analysis is reported in Table 4.6. All the residual values lie within the range of -2 to + 2 indicating that there are no abnormally high or low residuals. Histogram of the residuals indicates no heavy departure from normality.
Residual plot of standardized predicated value against standardized residual values is reported in Figure 2. Residual plot indicates a very healthy plot with no specific pattern of residuals (random scatter of residual values). This means that the assumption of linearity, normality and constant error variance are satisfied.

Table 4.6:Descriptive statistics for Residuals
Minimum Maximum Mean Std. Deviation N
Predicted Value 1.9791 4.3801 3.4175 .37269 206
Residual -1.95706 1.49564 .00000 .51183 206
Std. Predicted Value -3.860 2.583 .000 1.000 206
Std. Residual -3.777 2.886 .000 .988 206


Figure 4.6 Histogram of residuals

Fig4.5 Residual scatter plot of standardized predicted and standardized residual values
4.2.3 TESTING RESEARCH QUESTION 3
Q: Is the attitude towards price discounts has an influence on the purchase intention?

Null hypothesis Ho:
Attitude towards price discounts does not have significant effect on purchase intention (??=0)
Alternate hypothesis H1:
Attitude towards price discounts has a significant effect on purchase intention (?? '0)

Linear regression analysis is performed by taking purchase intention as dependent variable and Attitude towards coupons, Attitude towards product bundling, Attitude towards price discounts, Attitude towards Point of purchase displays and Attitude towards rebates as predictor (independent) variables.
The conceptual form of the model is
Purchase Intention = ??0 + ??1 (Attitude towards coupons) + ??2 (Attitude towards product bundling) + ??3 (Attitude towards price discounts) + ??4 (Attitude towards Point of purchase displays) + ??5 (Attitude towards rebates) + ??, where ??i is the regression coefficient and ?? is the random disturbance term assumed to follow normal distribution with mean zero and constant variance ??2.

Results of the regression analysis reported the fitted regression model as:
Purchase Intention = 1.379 ' 0.166 (Attitude towards coupons) ' 0.092 (Attitude towards product bundling) + 0.142 (Attitude towards price discounts) + 0.105 (Attitude towards Point of purchase displays) + 0.095 (Attitude towards rebates)

Coefficient of determination R2 = 0.346. This means that 34.6% of the total variation in preference in fitted (explained) by five independent variables. Result of the ANOVA F test is reported in Table 4.4. Results clearly indicate that the overall fitted regression model is statistically significant (F (1, 204) = 19.344, p =<.001). Results of the t test are reported in Table 4.5

Estimated regression coefficient for Attitude towards price discounts is ?? = 0.142. Result of the t test indicates that the null hypothesis of no significant effect (Ho: ?? = 0) cannot be rejected at 0.05 level of significance (t (204) = 1.821, p = .070). This means that attitude towards product bundling, controlling for the effect of attitude towards POP displays, Attitude towards rebates, Attitude towards price discounts and Attitude towards product bundling, does not exhibit a significant effect on purchase intention. The study hypothesis H1 is not supported.

4.2.4 TESTING RESEARCH QUESTION 4
Is the attitude towards POP displays has an influence on the purchase intention?

Null hypothesis Ho:
Attitude towards POP displays does not have significant effect on purchase intention (??=0)
Alternate hypothesis H1:
Attitude towards POP displays has a significant effect on purchase intention (?? '0)

Linear regression analysis is performed by taking purchase intention as dependent variable and Attitude towards coupons, Attitude towards product bundling, Attitude towards price discounts, Attitude towards Point of purchase displays and Attitude towards rebates as predictor (independent) variables. The conceptual form of the model is
Purchase Intention = ??0 + ??1 (Attitude towards coupons) + ??2 (Attitude towards product bundling) + ??3 (Attitude towards price discounts) + ??4 (Attitude towards Point of purchase displays) + ??5 (Attitude towards rebates) + ??, where ??i is the regression coefficient and ?? is the random disturbance term assumed to follow normal distribution with mean zero and constant variance ??2.
Results of the regression analysis reported the fitted regression model as:
Purchase Intention = 1.379 ' 0.166 (Attitude towards coupons) ' 0.092 (Attitude towards product bundling) + 0.142 (Attitude towards price discounts) + 0.105 (Attitude towards Point of purchase displays) + 0.095 (Attitude towards rebates)

Coefficient of determination R2 = 0.346. This means that 34.6% of the total variation in preference in fitted (explained) by five independent variables. Result of the ANOVA F test is reported in Table 4.4. Results clearly indicate that the overall fitted regression model is statistically significant (F (1, 204) = 19.344, p =<.001). Results of the t test are reported in Table 4.5

Estimated regression coefficient for Attitude towards point of purchase displays is ?? = 0.105. Result of the t test indicates that the null hypothesis of no significant effect (Ho: ?? = 0) cannot be rejected at 0.05 level of significance (t (204) = 1.829, p = .069). This means that attitude towards product bundling, controlling for the effect of attitude towards POP displays, Attitude towards rebates, Attitude towards price discounts and Attitude towards product bundling, does not exhibit a significant effect on purchase intention. The study hypothesis H1 is not supported.

4.2.5 TESTING RESEARCH QUESTION 5
Is the attitude towards rebates has an influence on the purchase intention?

Null hypothesis Ho:
Attitude towards rebates does not have significant effect on purchase intention (??=0)
Alternate hypothesis H1:
Attitude towards rebates has a significant effect on purchase intention (?? '0)

Linear regression analysis is performed by taking purchase intention as dependent variable and Attitude towards coupons, Attitude towards product bundling, Attitude towards price discounts, Attitude towards Point of purchase displays and Attitude towards rebates as predictor (independent) variables. The conceptual form of the model is

Purchase Intention = ??0 + ??1 (Attitude towards coupons) + ??2 (Attitude towards product bundling) + ??3 (Attitude towards price discounts) + ??4 (Attitude towards Point of purchase displays) + ??5 (Attitude towards rebates) + ??, where ??i is the regression coefficient and ?? is the random disturbance term assumed to follow normal distribution with mean zero and constant variance ??2.

Results of the regression analysis reported the fitted regression model as:
Purchase Intention = 1.379 ' 0.166 (Attitude towards coupons) ' 0.092 (Attitude towards product bundling) + 0.142 (Attitude towards price discounts) + 0.105 (Attitude towards Point of purchase displays) + 0.095 (Attitude towards rebates)

Coefficient of determination R2 = 0.346. This means that 34.6% of the total variation in preference in fitted (explained) by five independent variables. Result of the ANOVA F test is reported in Table 4.4. Results clearly indicate that the overall fitted regression model is statistically significant (F (1, 204) = 19.344, p =<.001). Results of the t test are reported in Table 4.5

Estimated regression coefficient for Attitude towards price discounts is ?? = 0.095. Result of the t test indicates that the null hypothesis of no significant effect (Ho: ?? = 0) cannot be rejected at 0.05 level of significance (t (204) = 1.458, p = .146). This means that attitude towards product bundling, controlling for the effect of attitude towards POP displays, Attitude towards rebates, Attitude towards price discounts and Attitude towards product bundling, does not exhibit a significant effect on purchase intention. The study hypothesis H1 is not supported.

4.2.6 TESTING RESEARCH QUESTION 6
Q: Is the purchase intention same across income groups?
Hypothesis H: Mean score for purchase intention is not same for different Income groups.

Null hypothesis: There is no significant difference in mean purchase intention score across different Income groups.
Alternate hypothesis: Mean purchase intention score across different Income groups is not same.

Table 4.12 reports descriptive statistics of purchase intention for each category of Income group. Mean, standard deviation and 95% confidence interval for mean purchase intention score are reported. Inspection of descriptive statistics indicates difference in preference across various Income groups.

Table 4.12 Descriptive Statistics

N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum
Lower Bound Upper Bound
less than 30000 99 3.4323 .68673 .06902 3.2954 3.5693 1.00 4.80
30000-50000 55 3.3782 .55867 .07533 3.2272 3.5292 2.00 4.40
50000-70000 21 3.3238 .40237 .08781 3.1407 3.5070 2.80 4.00
more than 70000 31 3.5032 .71530 .12847 3.2409 3.7656 1.00 4.60
Total 206 3.4175 .63314 .04411 3.3305 3.5044 1.00 4.80

One factor ANOVA is used to test the study hypothesis. The factor is taken as Income with 4 categories ' (i) Less than 30000 (code =1), (ii) 30000-50000 (Code =2), (iii) 50000-70000 (code =3), (iv) Above 70000 (code =4) .Assumption of equality of variance is tested using Levene's test. Results of the Levene's test reports that null hypothesis of no significant difference in variances cannot be rejected at .05 level of significance (F (3, 202) = 0.863, p =.461). The equality of variance assumption is satisfied.
As a remedy to violation of homogeneity of variance assumption, Welch's robust test for equality of means is performed. Results of one factor ANOVA test indicates that null hypothesis of equality of mean score for repurchase intention can be rejected at .05 level of significance (F (3, 202) =0.557, p = .645). This means that null hypothesis is not rejected and purchase intention is significantly different across different Income categories.

Table 4.13 ANOVA Test Results with purchase Intention as dependent variable

Sum of Squares df Mean Square F Sig.
Between Groups .519 3 .173 .428 .733
Within Groups 81.658 202 .404
Total 82.177 205



Figure 4.8 Means plot of Repurchase Intention and Monthly Income

RELIABILITY ANALYSIS:

Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items
.861 .863 5

The alpha coefficient for the 5 items is .863, which shows that the items have a relatively higher internal consistency.

'
5. INDUSTRY OVERVIEW
5.1 INTRODUCTION
One of the biggest things that had taken Indian business by storm is Ecommerce. It has created an entire new economy which has huge potential and is also changing the way businesses are done in India. The reason for its phenomenal raise is that both buyers and sellers have win-win situation. Rising incomes is an important factor that is driving the purchasing of goods and services online through internet which is considered more convenient for the consumers.

The industry is expected to grow at a 40% CAGR from $5.9bn in 2010 to $34.2bn by 2015. Indian ecommerce is considered to be in nascent stage as of now and it is considered to amplify in the coming years. There are some weak links which were being corrected with the help of technology and for sure we can expect that more easy, convenient and secured ecommerce business will be here to stay. The rise in the number of internet users, increase in the acceptability of online payments, favorable demographics and advent of internet enabled devices are some of the crucial factors that are driving the growth of the ecommerce industry in India. The number of online transactions is expected to increase from 11million in 2011 to 38 million by 2015.

Venture capitalists and private equity players have shown the faith in this sector and you can get this from the kind of investments they have done in this industry from the past. In 2011 $55bn are invested where as this number has gone up to $397bn in 2013. Some innovative ways of doing business had transformed this sector to greater heights. One of the things is Cash on Delivery that accounts to almost 50-80% of the online retail sales. Players have come up with most innovative business models taking ecommerce as their platform and started reaching their target customers in most attractive ways. Some key concerns that are surrounding this industry are the inventory management and logistics capabilities. In order to sustain the heat that this sector has got in the recent days government also should come up with some dedicated ecommerce laws like sales tax laws which need to revise as it is creating problems for retailers especially while deciding the warehouse location.


Figure 5.1: Modes of e-commerce transactions
Besides growing internet penetration the change in consumer mindset also had great impact on the growth of this industry. The country's ecommerce sector can be divided into travel and non-travel. Online travel has contributed 81% to revenues in 2011.
Some of the key enablers for this industry are
1. Internet Penetration: Internet penetration as of now in India is at 11.4% which is one of the lowest in the world. Government has come front in improving this penetration and recently it has declared 20000 Cr for internet cable laying
2. The supporting ecosystem for E-commerce
3. Devices: In addition to the growth of Laptop, Smartphone has revolutionized how internet is being accessed .Taking this as an advantage companies are spending money to be in contact with the customers continuously through their Mobile apps.
4. Transformation in the payment landscape: The cards per capita in India are one of the lowest in the world i.e 0.2%. The success rate of online transactions through credit cards or debit cards is still low at 74% but lot of measures are being taken both by banks and ecommerce companies to increase the rate. Net banking transactions have improved their success rate from 68% in 2011 to 76% in 2013

Indian E-commerce industry is a 2billion dollar industry which is a very small fraction compared to global industry size. Many aspects like growing internet penetration and mobile penetration are supporting this industry and expected to grow 10times in the coming decade
The internet has already charging the way connected Indians shop online. Not only opening up a new world of lower prices and choices but it has also emerged as wealth-creating for many online entrepreneurs. Many e-commerce companies which were started in the early of evolution of e-commerce in India haven't seen much profits, but slowly as the internet user base being increasing day by day they are seeing a double digit growth slowly. Many ecommerce portals mainly in retail domain are experiencing phenomenal growth with online transactions growing by leaps and bounds every month.

Most shopped categories in 2013 are electronic gadgets, lifestyle and jewelry. According to survey conducted by ASSOCHAM in 2013 Mumbai topped the list of most number of online shoppers followed by Delhi and Kolkata.


Source: ASSOCHAM Report, 2013
Figure: Behavior of online shoppers of various age groups

Top 10 ecommerce platforms as per ASSCHAM are
1. Flipkart
2. Ebay India
3. Snapdeal
4. Amazon India
5. Myntra
6. Shopclues
7. Dominos
8. Freecharge
9. Jabong
10. Tradus

5.2 GLOBAL SCENARIO
When we consider the impact of ecommerce on the global economy it is said to be very influential. E-commerce has been radical effect on the business that are located all over the world. It is more powerful than the traditional resources that were adopted by businesses all over the world. With the aid of ecommerce it is possible for people to shop online from their comforts of their home irrespective of their geographical location. Ecommerce has been able to remove the geographical barriers successfully and helped customers to come in contact with vendors without hassles.
The impact of ecommerce is one that has bought in a drastic change in the traditional market. It is one that has been welcomed with open arms as it has clicked immensely for many businesses and now most of the businesses who are not present on ecommerce cannot exist. Because of ecommerce many small businesses have been benefited as they no longer require physical offices to cater to the people of particular geographic location. The target audiences are also getting benefitted as they can be reached without wasting time and economic resources. Many companies are using effective internet marketing techniques so as to reach the target market with less effort.

The impact of ecommerce on the global economy has influenced everything from the production to the service levels that many companies are able to do business with. The change is very significant. With ecommerce these businesses are faced with the main challenge of having to stay ahead of their market competitors with the technological transformation. Countries that are not economically sound were been effected by the advent of ecommerce. Many companies have enhanced sales and increased their profits with ecommerce.

5.2.1GLOBAL E-COMMERCE SALES:

Source: Goldman Sachs, 2013

Global ecommerce sales are growing at more than 19% a year. Worldwide retail web sales will reach nearly $1trillion by end of 2013 but they reached that mark a year before that is by end of 2012 only it reached $1 trillion marks there by you can observe how ecommerce industry in growing globally. E-commerce was initially driven by the convenience of global shopping from your computer but now it is driven by convenience of global shopping from any location and at any time (Goldman Sachs, 2013)
Most of the ecommerce companies are doing ecommerce business which is business to consumer where they won't be getting more margins so it's high time for all the e-commerce companies to also to B2B sales so as to get profits which intern let the venture capitalists to pour more money that can be used to expand operations and serve better.

5.3CHALLENGES FACED BY E-COMMERCE INDUSTRY:
The top 10 reasons for lost online purchases
' Price disparities and poor presentation
' Site functionality issues such as lack of time-saving features
' Poor on-site search
' Poorly displayed product descriptions
' Presentation of additional charges
' Navigation issues such as poor sorting and filtering functionality
' Lack of discounts/sales
' Errors and bugs

In today's e-commerce sites routinely experience transaction closure rates in the range of 3 to 5 percent of the total visitors - the primary reason for the failure is most of the sites are too generic - no personalization whatsoever. Most of the shoppers spend a significant amount of time by going through multiple websites and related comparison websites and still they cannot make a quick decision - average time to make a decision to buy any product online is between 2 hours to a week - also, they confine to a couple of choices.

5.4 FUNDING PROBLEMS:
Many e-commerce companies are facing funding problems. So the companies and also VC's are finding Mergers and closures could be the right way to get their money back and therefore this has become a rippling effect of many companies merging which could become a threatening situation in the coming years to the commerce industry.
Some of the mergers that happened this year and last year are
' In April 2013, baby product-seller Babyoye.com reportedly merged with Bangalore-based Hoopos.com, which also sells baby products.
' In March 2013 Urbantouch.com, an online fashion retailer for men, was shuttered less than a year after it was acquired by clothes retailer FashionandYou.com.
' Buytheprice.com, a site connecting buyers and sellers, was bought by Tradus.com, site offering a similar service.
' In February, Inkfruit.com was taken over by fashion retailer Zovi.com.

Most of the e-commerce sites won't be sharing any financial information publicly as they were not listed, but venture capital investors in such firms privately admit that many of these ecommerce websites won't even reach break-even point soon. So investors are now actively pushing for mergers in India's crowded online marketplace in the hope of saving on operating costs, thereby turning a profit faster. But this is far from the scenario that investors and entrepreneurs had envisioned from India's e-commerce industry. It seemed even a small part of the country's 1.2 billion-strong population starts buying goods and services online, this industry would be gold. McKinsey estimated that by 2015, online retailers in India would have annual sales of up to $2 billion. Because of these kinds of reports, venture capital investors poured close to $700 million into 50 e-commerce sites in the past three years. Many of the startups founded in India during this time were focused on e-commerce.
Many film industry stars also pushed a lot of money into industry by picking up the stake in some of the big travel and ticket booking sites like yatra.com and ticketplease.com. Because Indians have not taken online shopping widely as hoped the experts now say it will take much longer to achieve the gains of online retail in India.
Retailers in India have begun offering a cash-on-delivery option as the people distrust using the use of credit cards online and also not sure about the quality of the product that they will get once ordered online. But this being the case the online retailers have to finance the sale till the order is delivered which is very expensive. Also, cash-on-delivery makes it easy for customers to reject products at the point of delivery. These manual interventions take away some of the efficiency gains of online selling.
E-commerce companies mainly startups which are in huge number are also facing some problems on supply side of the operations. So in-order to avoid this problems many online retailers often are investing in supply chain facilities. Companies like travel sites and those who offer services linking buyers and sellers need a very good sales team to build the network of providers. Neither of these both comes for cheap. The target audience for online sites who usually are young and affluent consumers are been lately flooded with huge malls all over big cities very they can spend some time besides shopping and definitely a trip to a mall is more than just shopping, it's a form of entertainment.
To attract buyers, many e-commerce sites offered deep discounts on their products. But price wars are expensive and there isn't any evidence than customers attracted by low prices, will remain loyal once the retailer starts charging full price.
One of the good examples we can take is of Bangalore-based start-up, Taggle.com, it was launched in 2010 as a group buying site, much like Group-on, which showcases daily shopping deals. Then it shifted its focus to online retail and found itself competing with dozens of other sites selling similar products, with the only difference being price. When Taggle.com tried to compete on price, it quickly burned through its capital and the founders decided to shut the business in 2011, declaring the business model unsustainable. The future of Indian online retail looks bright and the recent $50 million-investment, led by eBay-0.13% into Indian deals site Snapdeal.com is proof that many companies want a piece of this.
According to the recent report by Boston Consulting Group it's like 'The Internet is projected to reach small towns and the low levels of the economic ladder more quickly than retail chains '. With just 10% of India's population accessing the Internet, there's plenty of growth in the future.
Meanwhile, the false start of online retailing has had one positive side effect: it has created a tribe of entrepreneurs who have got a taste for building their own companies. Chances are they'll take that experience into their next venture as seasoned founders.

5.4 Trends in E-commerce industry:
Indian E-commerce has done very well in the year 2012 which capped off an incredible 5 years of exponential growth, from approximately $1.75 Billion in 2007 to over $14 Billion by 2012.Regardless of how things develop in the coming days 2013 will see more trends and innovations in Indian E-commerce.

Some of the trends for a maturing E-commerce space in India are
The Rise of M-Commerce
According to the research done my Kleiner Perkins, one of the world's largest VC firms revealed some fascinating insights for the Indian ecommerce industry. The people who are using desktop internet had overtaken by the people using mobile internet. Assuming this continues India is going to be the first true-mobile-first market. Several Indian ecommerce firms prepared to invest in creating world-class mobile platforms are best positioned to capture more market share.

Bridging Thee-Commerce Product Experience Gap
Customers must feel a certain degree of comfort of what they are buying will fit them look good on them look right in their living room etc., and clearly many Indian shoppers still have major concern over despite generous returns policies. In 2013, we might see some interesting initiatives to bridge the information gap between what the consumer wants and what the product is.
Example: Yepme.com has come up with try and Buy options to its customers.

Social becoming more about Online Reputation Management (ORM) and less about catching attention
Brand's online reputation management must be characterized by the existence of one seamless 'voice' across all channels: Facebook, Twitter, Linked-in etc. Customers are going to more and more value brands that stand for something, or are differentiated in ways other than low prices and wide product ranges.

Product Videos: Engaging and Informative
Most e-commerce companies are very much aware of the importance of including high quality product images on their product pages. Typically, products are photographed from several angles, with the photos touched up to give a glamorous look. Photos tell part of the story, but in 2013 it is believed that video will start to be a significant differentiator in e-commerce. One can create more impactful content via video. Product videos are not easy to execute, but if done well they can have a significant impact on product page conversion rates. It is expect to see this as a major trend in Indian e-commerce for 2013.

'Shoppable ' Images & Videos
Several large players like IKEA and GUCCI have experimented on this of each product viewing in its natural setting rather than just browsing through an array of product. Videos can be used in a similar way. With the typical high-production values of a fashion video, a company can display this season's must have products ' the difference being that the products in the video are clickable even as the video plays and when clicked, bring the viewer to the product pages of the selected product.

Emergence of B2B
It is believed that the B2B e-commerce wave could soon hit India because of the educational role that B2C has been playing for the past 5 years. The advancement of B2B e-commerce is very much supported by the prior establishment of a strong B2C market. The Indian e-commerce story so far has been dominated by B2C e-commerce companies. Indian B2C e-commerce is currently valued at $14 Bn.
Example: How Wall-mart manages electronically its inventory requirements with 60,000+ suppliers

Today's young Indian web-users are already booking cinema tickets and buying shoes online and are extremely comfortable using e-commerce. The same individuals, who are buying movie tickets and shoes for themselves today, will be the MDs and admin buyers of tomorrow. Thus, even if the B2B e-commerce wave does not break fully in 2013, its arrival over the coming few years is very much certain

6.1 DISCUSSION AND IMPLICATION OF FINDINGS
The study was conceptualized on the idea that companies are spending too much on online promotions without knowing which promotional tool gives best results in terms of increasing sales. The study commenced with a thought to know which tool among coupons, rebates, product bundling, Point of purchase displays and price discounts has more influence on the purchase intention of the customer. These clauses were taken as hypothesis and tested. It was found that out of all promotional tools only attitude towards coupons only have significant impact on the purchase intention of the customer with a p value as 0.013 and t= 2.504 rejecting the null hypothesis. This was a insight that shows the whatever was though before research was right that companies without knowing what is doing in the industry they are just spending bomb on their different sales promotional tools.
Logically, this effect of tools on purchase intention among income groups was thought to be different and to test this, ANOVA was conducted to test whether there is significant impact on the purchase intention among different income levels. The ANOVA showed that no significant difference across the various levels of income (F (3, 202) = 0.863, p =.461). Thus the income level is not right element to study the market.
Attitude towards coupons contributes 19.5% to the purchase intention where as attitude towards product bundling contributes 11.4%, attitude towards product discount contributes 16.1%, attitude towards point of purchase displays contributed 14% to the purchase intention. According to the research done by Muhammad Rizwan (2009) has revealed that attitude towards coupon has a significant impact on the purchase intention with p=0.018 and attitude towards price discount has a significant impact with p=0.007. Whereas in this study only attitude towards coupons has a significant impact on the purchase intention of the customer with p=0.013. The difference may be because of the kind of sample and place where research is done.
Dynamic nature of the industry and changing customer needs may also be reasons. But commenting on the reason of change now might not be good. This can be considered as a scope for future study. After this study an insight was that now a days we are seeing third party sites who source coupons of different websites to the customers by their own way of promoting the coupons and there by charging some margin from the seller if that coupon was being used by the customer in a particular site which is supporting this study
7.1 CONCLUSIONS
This research has been completed and results have been arrived at. Conclusions that are drawn from these results have been translated into recommendations with managerial implications are. Those recommendations can be used as strategic decisions in their future decisions at different companies which are operating in the ecommerce industry. Some of these results can be very useful in addressing issues that lurk at the back of the mind. Issues like which online promotional tool has got major influence on the purchase intention of the customer in the apparel industry have been clarified and accordingly companies can plan their promotional strategies in the future. This insight about the level and direction of the impact can be very useful even in future assessments using the same model.

Thus the study supports the current strategy of many companies in this industry who are using third parties to promote their coupons and increase their sales. One insight from this research that could be very helpful is that most companies are too much concentrating on price discounts which does not have not influenced purchase intention in this study

7.2 RECOMMENDATIONS
The findings from this study provide a number of managerial implications. These implications have been translated into recommendations which the management of various companies in this industry which can be used as guidelines in making decisions especially in terms of sales promotional strategies.
The following list shows some of the recommendations
1. Majority of the companies in this industry who are into apparel have been using coupons as one of their major sales promotional tool to increase their sales. Even they have some tie-ups with third party websites which they source the coupons for these third party websites in terms of promoting their website coupons. So as this study revealed that Attitude towards coupons has a significant influence on the purchase intention of the customer the companies can continue sourcing to third party sites apart from promoting coupons on their own.
2. It has been shown that attitude towards price discounts does not show any significant influence on the purchase intention. So companies should concentrate less on price discounts which is back firing them in terms of margins. Most of the companies are going on a price war and so they are coming up with frequent price discounts without creating any differentiation to the customer in terms of offering. As we are seeing closure of many companies especially in apparel sector like Urbantouch, Miraistore etc due to lack of funding, the companies should plan properly for how long they have to continue with a particular sales promotional tool.
3. It was also shown in the study that attitude towards product bundling doesn't have any influence on purchase intention. We can see that companies are trying out Product bundling with discounts which can be considered as an art of increasing profits which can be considered as a factor in the future studies that can influence purchase intention
4. The industrial space in which this research was conducted is very dynamic. What is true today might not hold true tomorrow. Industry should thus conduct similar studies periodically. To keep track of this market, companies should continuously monitor customer data and be on the lookout for changes in their strategies to increase sales.

REFERENCES
Schambach, S. (2007). Pumping up sales through potent promotions. Retrieved from ecommercetimes: http://www.ecommercetimes.com/story/59099.html
Barat, S., & Paswan, A. K. (2005). Do higher face-value coupons cost more than they are worth in increased sales? The Journal of Product and Brand Management, 14(6), 379-386. Retrieved Sep 2013, from http://search.proquest.com/docview/220597031?accountid=38885
Chaterjee, P. (2009). Advertised versus unexpected next purchase coupons: consumer satisfaction, perceptions of value, and fairness. Journal of Product and Brand Managament, 16(1), 57-69. Retrieved Sep 2013
Faryabi, M., Sadeghzadeh, K., & Saed, M. (2012). The effect of price Discounts and store image on consumers purchase intention in online shopping context case study: Nokia and HTC. Journal of Business Studies Quarterly, 4(1), 197-205. Retrieved Sep 20, 2013, from http://search.proquest.com/docview/1284540040?accountid=38885
Harimukthi, A. K. (2012, Oct). Analyzing the factors that Affecting Consumer's Purchase Intention in Toimoi Store, Indonesia. Journal of management and Behavioral Sciences, 13(14), 80-83. Retrieved Sep 20, 2013, from http://psrcentre.org/images/extraimages/1012544.pdf
Hayan Dib, M. A. (2013, Sep). The impact of sales promotion on perceived transaction value and purchase intentions: The moderating role of promotional benefit level. International Journal of Economy, Management and Social Sciences, 731-736. Retrieved Sep 20, 2013
Patrali chaterjee, J. M. (2010, July). Customized online promotions: Moderating effect on promotion type on deal value, perceived fairness, and purchase intent. Journal of Applied Business Research, 26(4), 13-19. Retrieved Sep 20, 2013, from http://journals.cluteonline.com/index.php/JABR/article/view/302/292
Rizwan, M. (2009). Affect of Sales Promotion Tools on Purchase Intention of Consumer. European Journal of Developing Country Studies, 6, 7-20. Retrieved Sep 19, 2013, from http://bellpress.org/Journals/index.php/EJDCS/article/view/20
S.Mcniell, L. (n.d.). Chinese consumer preference for price based sales promotion techniques- the impact of gender, income and product type. 1-7. Retrieved Sep 19, 2013
Shih-fen s.chen, k. B.-c. (1998). The effects of framing price promotion messages on consumers' perceptions and purchase intentions. Journal of Retailing, 353-372.
Soni Neha, V. M. (2013, July). Impact of sales promotion tools on consumer's purchase decision towards white good (Refrigerator) at drug and bhilai region of CG, India. Research Journal of Management Sciences, 10-14. Retrieved Sep 18, 2013
Teresa Montaner, L. d. (2011). Consumer Response to Gift Promotions. Journal of Product and Brand Management, 20(2), 101-110. Retrieved Sep 19, 2013
Vaidyanathan, R., Aggarwal, P., Stem, D. E., Muehling, D. D., & Umesh, U. N. (2000). Deal Evaluation and Purchase Intention:The Impact of Aspirational and Market based Internal Reference prices. Journal of Product and Brand Management, 9(3), 179-192. Retrieved Sep 2013, from http://search.proquest.com/docview/220594833?accountid=38885
Wu, W.-Y., Lai, M.-K., Wu, S.-J., & Fu, C.-S. (2012). Exploring the influential factors to the perceived value and purchase intention of online lucky bags. Interdisciplinary Journal of Contemporary Research In Business, 4(1), 515-531. Retrieved Sep 18, 2013, from http://search.proquest.com/docview/1029925118?accountid=38885
Yang-chu Lin, Y.-C. L.-X. (2013). The effect of impulsiveness and promotional types on purchase intentions in Taiwan. The IUP Journal of Marketing Management, 3(1), 7-24. Retrieved Sep 2013, from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2245547
Cathy, Vincent, W. M. (2006). Male versus female consumer decision making styles. Journal of Business Research, 59, 1297-1300. Retrieved Jan 2014, from http://dx.doi.org/10.1016/j.jbusres.2006.09.008

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