thesis ppt
TRANSCRIPT
A Study of Consumer Behavior Patterns in On-line Shopping
Thesis Submitted ByRicha Malik Under the supervision ofDr. (Mrs.) SudeshProfessor, University school of management,Kurukshetra.
Roadmap to Presentation
• Introduction
• Research objectives
• Research methodology
• Data analysis and interpretation
• Discussion and Conclusions
Introduction
• On-line shopping is the process consumers go through to
purchase products or services over the Internet.
• Online shopping behaviour (Internet shopping or buying
behaviour) refers to the process of purchasing products or
services via the Internet. The process consists of five
steps similar to those associated with traditional shopping
behaviour (Liang and Lai 2000).
Objectives of the study
Objectives of the study
1. To identify the variables influencing behaviour of consumers towards on-line shopping
2. To identify the factors influencing behaviour of consumers towards on-line shopping
3. To study the impact of identified variables under each category of factors on the on-line shopping
4. To study the impact of factors identified on the on-line shopping
5. To develop a comprehensive model that incorporates all the relevant variables and factors and their impact on online shopping
Research methodology
Research design
• Exploratory
- Review of existing literature
- In-depth interview • Descriptive
-Questionnaires
Data collection methods
• Primary sources
- In-depth interviews
- Questionnaires
• Secondary sources
- Literature review
SAMPLING DESIGN
• Sample size for – In-depth interview - 41 – Questionnaire - 598.
• Final questionnaire was pilot-tested on a sample of 35 to ensure the validity of the survey instrument. However, post elimination of incomplete responses, unreturned questionnaire and invalid answers, the final sample size used for analysis was 580.
Sampling Technique
• In the present study non-probabilistic sampling technique was used. Judgmental and snowball sampling technique were used in respondent selection for in-depth interviews and questionnaire.
• Initial set of respondents was selected on the basis of judgemental sampling. Subsequently additional units were obtained on the basis of information given by initial sample units and then further referrals were taken from those selected in the sample. In this way sample was grown by adding more and more referral-based respondents until it reached the limiting number (150/city).
CONTD…
• In the present study Judgement sampling was based on the following parameters:– The sample comprised of people who have done online
shopping– Only those people having credit cards were part of the
sample– The sample comprised of people whose minimum
qualification was at least Graduation– The sample was taken from metropolitan cities
assuming high internet diffusion rate
QUESTIONNAIRE DESIGN
• The questionnaire design was mainly based on the review of literature and the results of in-depth interviews and pilot study.
• The questionnaire was further checked for the convergent and discriminant validity and reliability.
Data analysis techniques
• Factor analysis• One –way ANOVA• Correlation• Multiple regressions
Data analysis and Interpretation
Categories Of Factors And Variables Under Each Factor Category Identified From Exploratory Study
S# Category of factor Variables under factor category
1 Demographic factor Age, Gender, marital status, income/salary, family size, education, ability to use internet
2 Psychographics factor Innovative, enjoyment, convenience, interaction with people, touch and feel
3 Online shopping feature and policies
Promotion, delivery policy, product return policy, detailed information of product, option of comparison,
4 Technological factor Quality, representative ness of product, value for money
5 Security factor Safety, privacy
Analysis And Interpretation For Descriptive Phase
S# Category Percentage
1 Airline/train reservations 81%
2 Banking & other financial
services
63%
3 Books/Magazines/
membership of library, news
dailies
33%
4 Software/Hardware/DVD/CD 16%
5 Dresses/Apparels/
Footwear/Jewellery
3%
6 Electronics/Mobile phones 30%
LIST OF CATEGORIES SHOPPED ONLINE
Consumers’ Response Variations For Online Shopping Across Gender
HYPOTHESIS
NO.
OUTPUT
VARIABLE
MEAN VALUE Mean Value (Total)
ANOVA RESULT
F (Sig.)
Male
(N=331)
Female
(N=249)
H3.2.1 Satisfaction with online shopping
3.71 3.75 3.72 .007 (.936)
H3.2.2 Future purchase on Internet shopping
3.8 3.5 3.67 .759 (.397)
H3.2.3 Frequency of online shopping
1.43 2.25 1.78 3.3 (.08)
H3.2.4 Number of items purchased on Internet shopping
2.43 2.5 2.46 .020 (.888)
H3.2.5 Overall spend on Internet shopping
2.57 3.5 2.97 1.411 (.252)
Consumers’ Response Variations For Online Shopping Across Different Family Sizes
HYPOTHESISNO.
OUTPUTVARIABLE
MEAN VALUE Mean Total
ANOVA RESULTF (Significance)
Nuclear family(N=193)
Family with two children(N=257)
ExtendedFamily(N=130)
H3.5.1 Satisfaction with online shopping
3.66 3.62 4 3.72 .328 (.725)
H3.5.2 Future purchase on Internet shopping
3.66 3.75 3.75 3.72 .037 (.963)
H3.5.3 Frequency of online shopping
1.5 1.375 2.25 1.61 1.595 (.236)
H3.5.4 Number of items purchased on Internet shopping
2 2.75 2.5 2.44 1.389 (.280)
H3.5.5 Overall spend on Internet shopping
1.83 3.5 2.75 2.77 3.030 (.078)
Relationship Between Online Shopping Variables And Enjoyment As One Of The Variables Of Psychographics Factor
Hypothesis
No.
Output variable Correlation coefficient
Significance Hypothesis testing results
H1.2.1 Satisfaction with online shopping
.284 .253 Hypothesis rejected
H1.2.2 Future purchase on Internet shopping
.037 .884 Hypothesis rejected
H1.2.3 Frequency of online shopping
.126 .619 Hypothesis rejected
H1.2.4 Number of items purchased on Internet shopping
.140 .580 Hypothesis rejected
H1.2.5 Overall spend on Internet shopping
.508 .031 Hypothesis not rejected
Relationship Between Online Shopping Variables And Convenience As One Of The Variables Of Psychographics Factor
Hypothesis
No.
Output variable Correlation coefficient
Significance Hypothesis testing results
H1.3.1 Satisfaction with online shopping
.058 .818 Hypothesis rejected
H1.3.2 Future purchase on Internet shopping
.169 .502 Hypothesis rejected
H1.3.3 Frequency of online shopping
.192 .445 Hypothesis rejected
H1.3.4 Number of items purchased on Internet shopping
.598 .009 Hypothesis not rejected
H1.3.5 Overall spend on Internet shopping
.110 .663 Hypothesis rejected
Relationship Between Online Shopping Variables And Interaction With Others As One Of The Variables Of Psychographics Factor
Hypothesis
No.
Output variable Correlation coefficient
Significance Hypothesis testing results
H1.4.1 Satisfaction with online shopping
-.161 .525 Hypothesis rejected
H1.4.2 Future purchase on Internet shopping
-.026 .920 Hypothesis rejected
H1.4.3 Frequency of online shopping
.100 .692 Hypothesis rejected
H1.4.4 Number of items purchased on Internet shopping
-.468 .050 Hypothesis not rejected
H1.4.5 Overall spend on net shopping
-.350 .154 Hypothesis rejected
Relationship Between Online Shopping Variables And Touch And Feel As One Of The Variables Of Psychographics Factor
Hypothesis
No.
Output variable Correlation coefficient
Significance Hypothesis testing results
H1.5.1 Satisfaction with online shopping
-.437 .069 Hypothesis not rejected
H1.5.2 Future purchase on Internet shopping
-.474 .047 Hypothesis not rejected
H1.5.3 Frequency of online shopping
-.077 .760 Hypothesis rejected
H1.5.4 Number of items purchased on Internet shopping
-.110 .664 Hypothesis rejected
H1.5.5 Overall spend on net shopping
-.431 .074 Hypothesis not rejected
Relationship Between Online Shopping Variables And Comparability Of Product As One Of The Variables Of Online Shopping Features
Hypothesis
No.
Output variable Correlation coefficient
Significance Hypothesis testing results
H2.5.1 Satisfaction with online shopping
.417 .085 Hypothesis not rejected
H2.5.2 Future purchase on Internet shopping
.163 .518 Hypothesis rejected
H2.5.3 Frequency of online shopping
-.225 .369 Hypothesis rejected
H2.5.4 Number of items purchased on Internet shopping
-.038 .881 Hypothesis rejected
H2.5.5 Overall spend on net shopping
.169 .502 Hypothesis rejected
Relationship Between Online Shopping Variables And Quality Of Product As One Of The Variables Of Technological Factor
Hypothesis
No.
Output variable Correlation coefficient
Significance Hypothesis testing results
H4.1.1 Satisfaction with online shopping
.484 .042 Hypothesis not rejected
H4.1.2 Future purchase on Internet shopping
.422 .081 Hypothesis not rejected
H4.1.3 Frequency of online shopping
.285 .251 Hypothesis rejected
H4.1.4 Number of items purchased on Internet shopping
-.283 .254 Hypothesis rejected
H4.1.5 Overall spend on Internet shopping
.348 .158 Hypothesis rejected
Relationship Between Online Shopping Variables And Representative ness Of Pictures And Colours As One Of The Variables Of Technological Factor
Hypothesis
No.
Output variable Correlation coefficient
Significance Hypothesis testing results
H4.2.1 Satisfaction with online shopping
.218 .385 Hypothesis rejected
H4.2.2 Future purchase on Internet shopping
.285 .251 Hypothesis rejected
H4.2.3 Frequency of online shopping
.028 .914 Hypothesis rejected
H4.2.4 Number of items purchased on Internet shopping
.504 .033 Hypothesis not rejected
H4.2.5 Overall spend on Internet shopping
.208 .407 Hypothesis rejected
Relationship Between Online Shopping Variables And Security As One Of The Variables Of Safety Factor
Hypothesis
No.
Output variable Correlation coefficient
Significance Hypothesis testing results
H5.1.1 Satisfaction with online shopping
.606 .008 Hypothesis not rejected
H5.1.2 Future purchase on Internet shopping
.215 .392 Hypothesis rejected
H5.1.3 Frequency of online shopping
.129 .609 Hypothesis rejected
H5.1.4 Number of items purchased on Internet shopping
-.161 .523 Hypothesis rejected
H5.1.5 Overall spend on Internet shopping
.006 .982 Hypothesis rejected
Relationship Between Online Shopping Variables And Privacy As One Of The Variables Of Safety Factor
Hypothesis
No.
Output variable Correlation coefficient
Significance Hypothesis testing results
H5.2.1 Satisfaction with online shopping
.606 .008 Hypothesis not rejected
H5.2.2 Future purchase on Internet shopping
.215 .392 Hypothesis rejected
H5.2.3 Frequency of online shopping
.129 .609 Hypothesis rejected
H5.2.4 Number of items purchased on Internet shopping
-.161 .523 Hypothesis rejected
H5.2.5 Overall spend on Internet shopping
.006 .982 Hypothesis rejected
Analysis Results
Dependent variable Independent variable Direction/relationCustomer satisfaction with online shopping
a) Securityb) Qualityc) Touch and feel factord) Option of comparisone) Privacy
PositivePositiveNegativePositivePositive
Future online purchase a) Customer satisfactionb) Qualityc) Touch and feel factor
PositivePositiveNegative
Frequency of online purchase None ---------
Number of Products/Items purchased
a) Convenienceb) Representative of pictures
and colours c) Interaction with people
PositivePositiveNegative
Total Spend on online purchase
a) Enjoymentb) Touch and feel factor
PositiveNegative
Results Of Regression Analysis
Dependent variable Independent variable Beta value (Significance) R2
Customer satisfaction with online shopping
a) Securityb) Touch and feel
factor
.646(.001)-.490(.009)
.606
Future online purchase
a) Customer satisfaction
.764(.000) .584
Frequency of online purchase
------
------ -------
Quantity of products/Items purchased
a) Convenienceb) Representative of
pictures and colours
c) Interaction with people
.577(.012)-.331(.139)-.137(.532)
.454
Total Spend on online purchase -------- -------- --------
Comprehensive model
Safety Factor
Security Customer satisfaction with online shopping
Psychographic Factor
Touch and feel factor
Interaction with people
Convenience
Technological Factor
Representative of quality
Future online purchase
Number of items purchased in online shopping
Final model
Online Shopping
Security Touch and feel factor
Interaction with people
Representative of pictures and colorsConvenience
If we assume all the output variables to be converging into one broad factor of online shopping, the final model could be interpreted as following:
PRACTICAL/ MANAGERIAL IMPLICATIONS
• Online retailers should focus on better positioning and representation of their products in online shopping.
• Websites/e-tailors should also ensure that all the relevant details regarding the quality and features should be provided to the online shoppers on the website.
• Customer shop through online shopping because of time and effort saving, hence the online retailers should design their website in such a way that the customers can reach out to his/her desired product in the minimum time.
• To save time and effort of the customers the online retailers should also give an option to the customers to store and compare their choices before the actual purchase.
• Online website should pay more attention to the female segments as results prove that females shop more in online shopping as compared to men. So companies should devise the policies and strategies to attract more number of people in this segment in future also.
• Online website need to ensure that security and privacy policies are appropriately defined, implemented, and communicated to the relevant stakeholder.
• Online shopping companies could also look into the possibility of spreading and promoting security and privacy related information on the Internet.
• Delivery policy of the online product should also include a defined trial period to assure that the customer doesn’t miss the touch and feel experience.
• Online retailers should also look into the possibility of running call centres which could ensure that the customer get a chance to formally interact with the other party before the actual purchase.
Continued…………
THEORETICAL IMPLICATIONS
• The study fills the literature gap between the past studies and present research by building a comprehensive model for online shopping.
• The study defines measurement parameters for online shopping.
• This is a unique study as it studied actual as well as post-purchase behaviour of the online shoppers.
• This is one of the initial studies done on online shopping in India as the concept of online shopping is quite new in India and none of the studies on online shopping are done so far on such a large scale in India.
• This is one of the important studies on online shopping in Indian context because it has included people from diverse backgrounds from different cities in India. It includes the random sample of individuals from major cities of India, which are representatives of whole population of India.
• This study finds unanimity amongst diversity by including people of different age groups falling under different income segments with difference in attitude and buying behaviour.
LIMITATIONS
• The study has few limitations like few Indian studies were available in Indian context so literature review pertains to foreign countries.
• Data has been collected from metro cities assuming high internet diffusion rate. And small cities and rural areas were not a part of sample.
• A cross-sectional study was done on the selected sample. Therefore results need to be tested over the time to check whether the same variables affect the online shopping in future or not.
• Judgemental sampling was used in the study. Therefore care must be taken while projecting the results of the study.
• Time and resources were also one of the limiting factors.
• Questionnaire technique was used. some hidden behavioural perspectives remained untouched and unnoticed in that case.
FUTURE RESEARCH DIRECTIONS
• Replication in Other Settings (Generalization)
• Need to Study Other Types of online shopping
• Need to Study Other Concepts and Relationships