This report is about the basic concept of Quantitative Tools for research, a key course for research students. Its also includes the SPSS results and their hypotheses.

1. 1
Quantitative Tools for Research
Usage of Credit Cards and Its Impact on Online Buying
Behavior of Consumer
Group Members
Muhammad Nadeem (16FMBA3E013)
Naveed Shahid (MMS-17F-003)
Muhammad Faisal Iqbal (MMS-17F-011)
Course Facilitator
Dr. Zahid Ali Channar

2. 2
Table of Contents
S. No. Description Page No.
1. Abstract 3
2. Introduction 3-4
3. Literature Review 4-5
4. Research Gap 5-6
5. Conceptual Framework 6
6. Objective 6
7. Hypothesis 6
8. Variables 7
9. Research Instrument 7
10. Data Collection 7
11. Sampling Design 7
12. T Test 8
13. Independence Sample T Test Anova 8-9
14. One Way Annova 9-11
15. Questionnaire 12-13

3. 3
Abstract
Consumer financing have become increasingly important in the private sector of Pakistan for the
last two decades. With the new reforms in the banking sector, the marketing of financial products
has become very competitive, creating a need for strategizing the marketing efforts. This study
investigates the usage of credit cards among employed and unemployed persons living in
Pakistan with a particular focus on the impact of gender on how people use credit cards. A
survey of consumers holding (at least) one or no credit card were used for data collection.
Variables related to demographics such as age, income level and gender have also been taken
into consideration. T Test, Independence Sample t Test and One way Annova have been used to
investigate the relationship between variables.
Introduction
When studying the online buying behavior of consumer, we should bear in mind that payment
through the credit card is a main source of payment in online purchasing. To study about the
usage of credit card and its impact on online buying behavior in Pakistan in order to gain insights
into their attitudes and preferences, decision-making frame work, attributes for store selection
and life styles encouraging online shopping. The target population for this study consists of the
urban consumers who are educated and belong to Upper and upper-middle socio-economic
classes which can be divided into two broad categories, first who are employed. In second
category those people are included who are unemployed. The first stated segment is the major
segment and insights about its behavior towards online shopping can provide a sound basis for
further research. A substantial size of this segment comprises of job holders and below 40 years.
An effective presence on social media websites is necessary and used as a channel to draw more
traffic which is another reason for majority of consumers whose age below 40. Since this
industry is still in its infancy in Pakistan, the innovators and early adopters who are in bulk at
this time mostly belong to the description above. This consumer segment is experiencing an
increase in spending power which is mostly consumed by purchases of clothing and accessories,
food and electronic products such as cameras, cell phones, tablets, computer or computer related
stuff. Thus we see a resemblance among these products and the product lines of major online
shopping sites.

4. 4
The second segment consists of people who used credit cards but they are unemployed and
below 25 years but they spend sufficient income to purchase products online. Their purchases are
less both in frequency and quantity as compared to the former segment
Literature Review
Credit cards, including store cards and bankcards, serve two distinct functions for consumers: a
means of payment and a source of credit (Ausubel 1991; Chakravorti 1997, 2000; Chakravorti
and Emmons 2001; Slocum and Matthews 1970; Stavins 2000). Based on the main use of credit
cards and the benefits sought, credit card users can be segmented into two groups: convenience
users and revolvers (Lee and Hogarth 1999). Convenience users tend to employ credit cards as
an easy mode of payment; typically pay their balance in full upon receiving the statement.
Revolvers, on the other hand, use the card principally as a mode of financing and chose to pay
interest charges on the unpaid balance. According to the consumer behavior literature, consumer
usage behavior and the benefits sought from a product or a service are one of the best predictors
to explain consumer purchase behavior (Peter and Olson 1999). Credit cards also serve as an
open-ended, easily available credit source ( Lee and Kwon 2002). When consumers use credit
cards as a mode of financing, credit cards compete with bank loans and other forms of financing
(Brito and Hartley 1995). Credit cards allow consumers to borrow within their credit limit
without transaction costs, which includes all the time and effort involved with obtaining a loan
from a financial institution. This convenience attracts many consumers to pay high interest on
outstanding credit card balances, rather than taking the time to apply for a loan with a lower
interest rate. As a result, credit cards account for a substantial and growing share of consumers’
debt (Canner and Luckett 1992). The popularity of credit cards as a payment medium has been
attributed to the convenience of not carrying cash and checks, the limited liability of lost/ stolen
cards, and additional enhancements, such as dispute resolution services and perks (i.e., frequent-
use awards programs) (Chakravorti 1997, 2000; Chakravorti and Emmons 2001; Whitesell
1992). They are frequently used for convenience, telephone and Internet transactions. The
behavior and the attitude of the consumer towards the use and acceptability of credit cards differ
for psychographic reasons (Yang, James and Lester 2005). Xiao, Noring and Anderson (1995)
devised a 38-item scale to measure affectiveness, cognitive and behavioral attitudes towards
credit cards. Affective attitudes involve emotional feelings (e.g. my credit card makes me feel

5. 5
happy); cognitive attitudes involve thoughts (e.g. Heavy use of credit cards results in heavy
debt); while behavioral attitudes involve actions (e.g. I use my credit card frequently). Many
consumers value uncollateralized credit lines for making purchases when they are illiquid (i.e.
before their incomes arrive), even at relatively high interest rates. Because of limited alternatives
to short-term uncollateralized credit, the demand for such credit may be fairly in-elastic with
respect to price (Brito and Hartley1995). Ausubel (1991) suggests that consumers may not even
consider the interest rate when making purchases because they do not intend to borrow for an
extended period when they make purchases. However, they may change their minds when the
bill arrives. Stavins (1996) argues that consumers are somewhat sensitive not only to changes in
the interest rate but also to the value of other credit-card enhancements such as frequent-use
awards, expedited dispute resolution, extended warranties, and automobile rental insurance.
However, she agrees with Ausubel (1991), Calem and Mester (1995) that lowering interest rates
may attract less creditworthy consumers, therefore dissuading some credit-card issuers from
lowering their interest rates. According to Jeans S. Bowers (1979) longitudinal study, low
income users of credit cards tend to use the cards for the installment feature rather than for
service features such as convenience, safety, or identification. It has been suggested that the
installment feature of credit is needed by the low income consumer to permit purchases such as
automobiles, furnishings, and other consumer durables. Demographics also seem to play a vital
role in making a choice and the use of credit cards as a convenience user or revolver. Age,
income level has been studied previously and suggest some indication for correlation between
demographic and use of credit card. According to the study conducted by Jean Kinsey (1981) the
probability of having credit cards and the number held was correlated highly with age and
occupation. However these two characteristics were less important than the place of residence,
use of checking and savings accounts, and attitude towards credit.

6. 6
Research Gap
As Pakistan’s online purchasing market is very small in size compared to other developing
countries and has a significant gap which is need to be filled through research work on the usage
of credit card and its impact on buying behavior and we don’t found any previous study
regarding impact of credit cards on online buying behavior in Pakistani market that’s why study
we select this topic to find out this gap.
Conceptual Framework
Objectives
The object of the study is to identify the impact of usage of credit card on online buying
behavior of consumer in pakistan. With the help of this research we can explore the factors
impacting online purchasing behavior in the Pakistani market. Moreover, by the help of this
research online businesses can improve their virtual presence and enhance online consumer
purchases and revisits.
Hypothesis:
Hypotheses
H2 Ha: Impact of online buying behavior is different for both gender (Male & Female).
H1 Ha: There is a significant difference between observed and expected frequiencies.
H3 Ha: Employed persons have same usage of credit card towards online buying as unemployed
and businessmen.
Online Buying
Behavior
Usage of Credit
Cards
Independent
Variable
Dependent Variable

7. 7
Research Instrument
To measure Online Buying Behavior we use adopted questionnaire developed by researcher
Swinyard and Smith in 2003. Which consist of four items in order to measure Online Buying
Behavior.
Data Collection
The research is mainly based on primary data. The primary data was obtained from survey
Questioners. In this research extensive survey was conducted for the collection of data through
unstructured Questionnaire. A Questionnaire will be distributed among employees of the
Organization, Friends and University students.
Sampling design
We used convenience sampling technique to design sample from the population.
Reliability Test
Reliability Statistics
Cronbach's
Alpha N of Items
.707 4
1. One Sample T-Test
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
OBB 36 15.0556 2.25445 .37574
S. No. Variables Instrument
1. Usage of Credit Card
1. Work Status
2. Credit Card Ownership
3. No. of Credit Card Posses
Serkan ¸Cankaya and Meltem
Ucal and Mary O’Neil (Likert)
(July 25, 2011)
2. Online Buying Behavior Swinyard & Smith (2003),

8. 8
One-Sample Test
Test Value = 20
t Df Sig. (2-tailed) Mean Difference
95% Confidence Interval of the
Difference
Lower Upper
OBB -13.159 35 .000 -4.94444 -5.7072 -4.1816
Interpretation
The value of One sample T-test is significant i.e. 0.000 therefore we are failed to reject our alternative
hypothesis that there is a significant difference (M= -4.94444) in expected (M=20) and observed
(M=15.0556) mean. Finally we reject null hypothesis.
2. Independence Sample T-Test
Group Statistics
Whatisyourgender N Mean Std. Deviation Std. Error Mean
OBB Male 29 14.9655 2.27538 .42253
Female 7 15.4286 2.29907 .86897
Interpretation
The value of independence sample T-test is not significant i.e. 0.867 therefore we reject our alternative
hypothesis that Online Buying Behavior is different in both gender. The figures show that Online Buying
Behavior is not different in male and female when they are shopping through Credit Card in Pakistan.
Finally we accept null hypothesis.

9. 9
3. Oneway Annova
Descriptives
OBB
N Mean
Std.
Deviation Std. Error
95% Confidence Interval for
Mean Minimu
m
Maximu
mLower Bound Upper Bound
Employed
21
15.095
2
1.78619 .38978 14.2822 15.9083 12.00 19.00
Unemployed
8
15.125
0
2.69590 .95314 12.8712 17.3788 11.00 19.00
Business 7 14.8571 3.23669 1.22336 11.8637 17.8506 9.00 19.00
Total 36 15.0556 2.25445 .37574 14.2928 15.8184 9.00 19.00
Test of Homogeneity of Variances
OBB
Levene Statistic df1 df2 Sig.
1.942 2 33 .159
ANOVA
OBB
Sum of Squares df Mean Square F Sig.
Between Groups .347 2 .174 .032 .968
Within Groups 177.542 33 5.380
Total 177.889 35

10. 10
Multiple Comparisons
Dependent Variable: OBB
Tukey HSD
(I) WorkStatus (J) WorkStatus
Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Employed Unemployed -.02976 .96369 .999 -2.3945 2.3349
Business .23810 1.01231 .970 -2.2459 2.7221
Unemployed Employed .02976 .96369 .999 -2.3349 2.3945
Business .26786 1.20045 .973 -2.6778 3.2135
Business Employed -.23810 1.01231 .970 -2.7221 2.2459
Unemployed -.26786 1.20045 .973 -3.2135 2.6778

11. 11
Interpretation
The difference in means of working status and online buying behavior is not significant because 0.968
value shows that it is not significant at 0.05 level of significance as it is greater than 0.05 .so we reject our
alternative hypothesis that online buying behavior is different with working status. Means online buying
behavior is not different between working status and except null hypothesis.
Findings
Independence sample T-Test was used to test H2. The analysis shows that the mean score of both genders
on Online Buying behavior are males (M= 14.9655) and females (M=15.4286) at P=.133, which is greater
than 0.05. Therefore, we accept the null hypothesis As P value is high. It means Impact of stress level is
not different for gender.

12. 12
Questionnaire
1. Name: _________________________________
2. E-mail: _________________________________
3. Contact # _________________________________
4. Gender
Male female
5. Work Status
Employed Unemployed Business
6. No. of Credit Card posses
1 2 3 >3
Online shopping behavior
Q1: Usage of credit card for shopping is easy.
Strongly Agree Agree Neutral Disagree Strongly Disagree
Q2: When I make a purchase, my friend’s and family’s opinion is important to me.
Strongly Agree Agree Neutral Disagree Strongly Disagree
Q3: I will have no problem in shopping through credit card if I get to know that my friends and
relatives are doing it without any problems.
Strongly Agree Agree Neutral Disagree Strongly Disagree
Q4: I would not shop online if Web page download time is slow.
Strongly Agree Agree Neutral Disagree Strongly Disagree