AMRP Final Presentation and Report
Given below is the link for AMRP Final Presentation…
The same has been embedded below:
.
.
Given below is the link for AMRP Final Report…
The same has been embedded below:
Suggestions and Recommendations
- Credibility in Payment System
Online frauds and breach are the biggest barriers to online sales. As a result, prospective buyers prefer staying away from revealing their credit card and bank details.
- Discount and lucrative offers
Use of credit card and E-banking can be encouraged by giving discount and lucrative offers while shopping online using it. A large number of users search for online discounts and then go for shopping.
- Untimely Delivery of Products
It might take a few minutes to search, book and pay for products and services online, but the delivery of the product may take unreasonable time.
- Consumer Bias
Consumers often display a bias for brands that they know well and have had a good experience in the past. Thus products of brands with a favorable bias will score over the products of less popular brands. A few would risk buying expensive jewelry from an unknown jeweler online.
- Lack of ‘Touch –Feel-Try’ Experience
The customer is not sure of the quality of the product unless it is delivered to him and post-delivery of the product, it is sometimes a lengthy process to get a faulty or the unsuitable product changed. Thus, unless the deliverables are as per the customers’ expectations, it is hard to infuse more credibility in online shopping.
- Mounting Competitive Pressures
To attract customers, the competing online players are adopting all means to provide products and services at the lowest prices. This has resulted in making the consumers choice-spoilt, who in turn surf various websites to spot the lowest price for the product. Thus, although the number of transactions is increasing, the value of the products sold is continuously falling owning to high competition and leaner margins.
- Seasonality
Online market is facing seasonal fluctuations. Usually August to February is the peak seasons for sale, while March to July is the dry seasons for sale. During the peak season, occasions that drive the sales are Diwali, Rakhi, Valentine’s Day, New Year, Christmas, Mother’s Day, and Friendship Day etc. On these occasions younger generations prefers buying and sending gifts online.
Moreover, companies need to reduce the risks related to consumer incompetence by tactics such as making purchase websites easier to navigate, and introducing Internet kiosks, computers and other aids in stores. The feedback of an online buyer should be captured to identify flaws in service delivery. This can be done through online communities and blogs that serve as advertising and marketing tools and a source of feedback for enterprises.
Results And Interpetations
- There is a strong inter-dependence between a few variables affecting online buying behavior. For example, owning a credit card, gender and E-banking has a significant impact on the frequency of online purchases whereas age and income of the respondent does not. Also, gender does not have any impact on the average amount spent per purchase made online.
- Depending on the reasons for a person to be online, consumers have been divided into homogeneous groups. Based on cluster analysis we could divide the respondents in three clearly distinct groups. These are ‘Leisure Hunter’, ‘Regular Web Person’ and ‘Dedicated Surfer’.
- Consumers have been further divided into four clusters on the basis of factors which influence them while making an online purchase as ‘The Surgical Shopper’, ‘The Enthusiast Shopper’, ‘The Casual Shopper’ and ‘The Reluctant Shopper’.
- We could also arrive at five factors which can explain the data with 66.88% significance. These factors could be categorized into ‘Trust’, ‘Convenience’, ‘Risk propensity’, ‘The Power Shopping’ and ‘Neglect’.
- The most popular product category sold online is Air/Rail Tickets followed by books.
- It must be noted that both the above products have relatively low touch-and-feel need. Gifts, Electronic Products and Car & Hotel rental are also very popular with the Online.
- Discriminant analysis shows that Gender, Credit Card, E-banking, Use of SNS and Age significantly differentiate between those who shop online and those who do not.
Discriminant Analysis
Dependent variable: online buyer or none buyer
Independent variables: Education, Gender, Monthly Income, owning a Credit Card, E-banking, use of social media sites and Age.
The significance of univariate F ratios indicates that when the predictors are considered individually, only Gender, Credit Card, E-banking, Use of SNS and Age significantly differentiate between those who shop online and those who do not.
Because there are two groups, only one discriminant function is estimated. The eigenvalue associated with this function is 0.691 and it accounts for 100 percent of the explained variance. The canonical correlation associated with this function is 0.639. The square of this correlation, (0.639)2= 0.408, indicates that 40.8% of the variance in the dependent variable is explained or accounted for by this model.
Cluster Analysis
Depending on the reasons for a person to be online, consumers can be clustered into homogeneous groups.
The various attributes used in CLUSTER Analysis have been coded as follow:
V1: News or Information
V2: Websites of company regarding product
V3: Travel and leisure
V4: Spent time in social media sites like Facebook
V5: Online shopping sites such as Flipkart
V6: Education related sites
V7: Official works, email
The three resulting clusters can be described as follow:
Cluster 1: internet users who are Leisure Hunter (relatively high values on variables V1, V4 and V5)
Cluster 2: internet users who are Regular Web Person (medium values on the variables)
Cluster 3: internet users who are Dedicated Surfer (relatively high values on variables V2, V3 and V6)
Users can further be clustered on the basis of factors which influence them while making an online purchase:-
The various attributes used in CLUSTER Analysis have been coded as follow:
V1: Brand Name
V2: Service delivery time
V3: Website Content
V4: Recommendation by friends
V5: Online Ads – posters/banners
V6: Online reviews by users of product
V7: Ease of payment and security
The four resulting clusters can be described as follow:
Cluster 1: The Surgical Shopper (relatively high values on variables V4 and V6)
Cluster 2: The Enthusiast Shopper (relatively high values on variables V1, V2, V3, V5, and V7)
Cluster 3: The Casual Shopper (relatively high values on variables V1, V2, V3, and V7)
Cluster 4: The Reluctant Shopper (relatively low values on all the variables)
Factor Analysis
To find the major factors on which customer’s online buying characteristics can be loaded, Factor Analysis was done on a 5-point Likert scale (1= Strongly Agree and 5= Strongly Disagree)
The various attributes used in factor analysis have been coded as follow:
V1: I prefer making a purchase from internet than using local malls or stores
V2: I can get the latest information from the Internet regarding different products/services that is not available in the market
V3: Online shopping is more convenient than in-store shopping
V4: Online shopping saves time over in-store shopping
V5: It is safe to use a credit card while shopping on the Internet
V6: Online shopping allows me to shop anywhere and at anytime
V7: I trust the delivery process of the shopping websites
V8: Products purchased through Internet are of guaranteed quality
V9: Internet provides regular discounts and promotional offers to me
V10: Cash on Delivery is a better way to pay while shopping on the Internet
V11: Sometimes, I can find products online which I may not find in-stores
V12: I have faced problems while shopping online
V13: I continue shopping online despite facing problems on some occasions
V14: I do not shop online only because I do not own a credit card
Attributes loading on various factors/components:
Loaded on factor 1:- V5, V6, V7, V8,
Loaded on factor 2:- V1, V2, V3,
Loaded on factor 3:- V12, V13,
Loaded on factor 4:- V4, V9, V10, V11
Loaded on factor 5:- V14 (negative loading)
Depending on the eigenvalues >1, there are 5 resulting factors which respondents look for:
Factor 1: Trust
Factor 2: Convenience
Factor 3: Risk propensity
Factor 4: The Power Shopping
Factor 5: Neglect
Regression Analysis
Dependent variable: Average Amount spent per purchase made online
Independent variables: Frequency of Purchase of products and services online, Family Income, owning a Credit Card, Marital Status, Gender, Occupation, Education and Age.
The value of R square is quite low and so it can be said that the regression model does not fit into the data very well. Also, the sum of squares of regression is lesser than the sum of squares of residuals and this reiterates the findings of R square. This is because if the sum of squares of regression is lesser than the sum of squares of residuals, then the independent variables do not explain the variation in the dependent variable well. While cross tabs suggest a positive relationship between multiple pairs of factors, the linear correlation model, with all factors together, does not fit in with the outcomes.
Data Interpretations and Analysis
Cross-Tabs With Chi-Square
H1: Owning a credit card does not have any impact on the frequency of online purchase.
As the p-value is lesser than 0.05, which is our assumed level of significance, we do not accept the null hypothesis, i.e. for the sample population, owning a credit card has an impact on the frequency of online purchase.
H2: Age of the respondent does not have any impact on the frequency of online purchase.
As the p-value is greater than 0.05, which is our assumed level of significance, we accept the null hypothesis, i.e. for the sample population, Age of the respondent does not have any impact on the frequency of online purchase.
H3: Gender does not have any impact on the average amount spent per purchase made online.
As the p-value is greater than 0.05, which is our assumed level of significance, we accept the null hypothesis, i.e. for the sample population, Gender does not have any impact on the average amount spent per purchase made online.
H4: Gender does not have any impact on the frequency of purchase of online products and services
As the p-value is lesser than 0.05, which is our assumed level of significance, we do not accept the null hypothesis, i.e. for the sample population, Gender has an impact on the frequency of purchase of online products and services.
H5: Income of respondents does not have any impact on the frequency of purchase of online products and services.
As the p-value is greater than 0.05, which is our assumed level of significance, we accept the null hypothesis, i.e. for the sample population, Income of respondents does not have any impact on the frequency of purchase of online products and services.
H6: E-banking does not have any impact on the frequency of online purchase.
As the p-value is lesser than 0.05, which is our assumed level of significance, we do not accept the null hypothesis, i.e. for the sample population, E-banking has an impact on the frequency of online purchase.
COLLECTED DATA
Based on the questionnaire (http://bit.ly/SshvWJ) floated to students and employees over the Internet, a sample data of 110 respondents have been collected.
Collected data can be accessed from the given link: http://goo.gl/9ejZI
The same has been embedded below..
Data Reduction
The key steps of data processing that would be implemented after data collection of 100 respondents through online survey:
CODING: For questions involving qualitative values the responses would be codified using numerical categories or values. For example, “I trust the delivery process of the shopping websites”, the response of “strongly agree” would be coded as 1 and “strongly disagree” as 5.
TRANSCRIBING: The data collected from all the 100 respondents, after codified would finally be transferred on MS Excel on computer to be fed as an input to SPSS.
About The Author
iitmaverick 