CRM CXM case study-------------------------------------

Business Intelligence Journal
January, 2010 Vol.3 No.1
Volume 3 - Number 1 - January 2010 - Semiannual Publication
Published by the IIU Press and Research Centre, A.C., Brussels EU Commission Building, Rond Point, Schuman
6, Box 5, 1040 Brussels, Belgium, for the Department of Business Management and Economics (BME) of the
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in collaboration with the Business Intelligence Service of London, UK (Sayco UK).
Editorial Note 1
Profile of authors included in this number 2
Information for Contributors 4
Articles The Association Between Components of Income Statement, Components of Cash Flow Statement
and Stock Returns
9
Mohsen Dastgir, Hossien S. Sajadi, Omid M. Akhgar
Market Analysis of Student’s Attitudes about Credit Cards 23
J.C. Arias, Robert Miller
Customer Experience Management in Retailing 37
Kamaladevi B.
Income Smoothing, Real Earnings Management and Long-Run Stock Returns 55
Abbas Aflatooni, Zahra Nikbakht
Building a World Class University 75
Ron Messer
Oil Prices and Exchange Rates: The Case of OPEC 83
Leili Nikbakht
Cybercrime in Nigeria 93
Okonigene Robert Ehimen, Adekanle Bola
Supplier Development Strategies: A Data Envelopment Analysis Approach 99
Rohita Kumar Mishra, Gokulananda Patel
Switching Cost and Customers Loyalty in the Mobile Phone Market: The Nigerian Experience 111
Oyeniyi, Omotayo Joseph - Abioudun, Abolaji Joachim
Level of Job Satisfaction and Intent to Leave Among Malaysian Nurses 123
Muhammad Masroor Alam, Jamilha Fakir Mohammad
Business Intelligence Journal - January, 2010 Vol.3 No.1

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Profile of authors included in this number
2 Business Intelligence Journal January
Article 1: TheAssociation Between Components Of Income Statement,Components Of Cash Flow Statement
And Stock Returns
Author: 1 - Mohsen Dastgir – Professor in Accounting. e-mail: mdastgir@scu.ac.ir
2 - Hossien S. Sajadi –Associate Professor in Accounting.
2 - Omid M.Akhgar – PhD student.
Article 2: Market Analysis of Student’s Attitudes about Credit Cards
Author: 1 – J.C.Arias – PhD, DBA
2 – Robert Miller – Candidate to DBA.
Article 3: Customer Experience Management in Retailing
Author: Kamaladevi B – B. Com., DTE, DECT, MBA, PGDPMIR, PGDRM, M.Phil,
Student, Dravidian University, Kuppam,Andhra Pradesh, India.
e-mail: kamaladevimba@gmail.com
Article 4: Income Smoothing,Real Earnings Management and Long-Run Stock Returns
Author: 1 - Abbas Aflatooni – Department of Accounting, College of Economics and Social
Sciences, Shahid Chamran University ofAhwaz,Ahwaz, Iran. e-mail:Abbasaflatooni@gmail.
com.
2 - Zahra Nikbakht – Payam Noor University (PNU), Koohpaye, Isfahan, Iran.
e-mail: Zahra.Nikbakht77@gmail.com
Article 5: Building aWorld Class University
Author: RonMesser–RonMesserholdsgraduatedegreesinbothpublicandbusinessadministration.
He is also a Chartered Accountant and a Certified Management Accountant. Mr. Messer has
experience in strategic planning, business analysis and information systems. His essays have
appeared in journals in Canada, the United States and the United Kingdom. Mr. Messer is
currently a faculty member in the School of Business at Kwantlen Polytechnic University,
which is located inVancouver, British Columbia, Canada. e-mail: ron.messer@kwantlen.net.

Article 6: Oil Prices and Exchange Rates:The Case of OPEC
Author: Leili Nikbakht – Department of Economics, College of Management and Economics,
Shahid Bahonar University of Kerman, Kerman, Iran. e-mail: leili.nikbakht@gmail.com.
Article 7: Cybercrime in Nigeriat
Author: 1 – Okonigene Robert Ehimen – Ambrose Alli University, Ekpoma, Edo State, Nigeria.
e-mail: robokonigene@yahoo.com.
2 – Adekanle Bola – Ambrose Alli University, Ekpoma, Edo State, Nigeria.
Article 8: Supplier Development Strategies:A Data Envelopment Analysis Approach
Author: 1 - Rohita Kumar Mishra – Lecturer, IIPM-School of Management, Kansbahal,
Orissa, (India) 770034. e-mail: rohitkmishra@rediffmail.com.
2 - Gokulananda Patel – Professor, Birla Institute of ManagementTechnology
Greater Noida, UP (India) 201306. e-mail: gn.patel@bimtech.ac.in.
Article 9: Switching Cost and Customers Loyalty in the Mobile Phone Market:The Nigerian Experience
Author: 1 – Oyeniyi, Omotayo Joseph – Department of Business Studies,
Covenant University, Ota. e-mail: omotayooyeniyi@yahoo.com.
2 – Abiodun,Abolaji Joachim – Department of Business Studies,
Covenant University, Ota. e-mail: abijoac@yahoo.com.
Article 10: Level Of Job Satisfaction And IntentTo Leave Among Malaysian Nurses
Author: 1 - Muhammad Masroor Alam – Institute of Business andTechnology (BIZTEK)
Karachi-Pakistan. e-mail: m_alam_muhammad@yahoo.com.
2 - Jamilha Fakir Mohammad – Univrsiti Utara Malaysia Kaula Lumpur-Malaysia
e-mail: illafm2000@yahoo.com
In order to make contact with any of the Authors referred to above, please forward your request to: edit.bij@saycocorporativo.
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BIJ’s Editor will be glad to submit your requests or inquiries before authors.

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2009 9
Dastgir M., Sajadi H.S., Akhgar O.M. - The Association Between Components of Income Statement, Components of Cash Flow Statement and Stock Returns
THE ASSOCIATION BETWEEN COMPONENTS OF
INCOME STATEMENT, COMPONENTS OF CASH
FLOW STATEMENT AND STOCK RETURNS
Mohsen Dastgir, Hossien S. Sajadi, Omid M.Akhgar
Abstract
This paper investigates the association between components of income statement, components of cash
flow statement and stock returns.A sample of 65 companies listed inTehran Stock Exchange for the time
period of 2003-2005. Regression analysis is conducted to test the research hypotheses. Results show that
among components of income statements, the net income (loss), and among components of cash flow
statement, cash flows from investing activities have a strong relationship with stock returns. However, the
paper results show that there is a stronger association between stock returns and components of income
statements relative to components of cash flows statement.

Introduction
The main purpose of financial reporting
is to state the enterprise’s financial position
and performance to the users of financial
information to help them in their decision
making. Main instruments for transferring
such information to the user groups are
financial statements and supplemented notes
which are the final product of accounting
process and financial reporting
The income statement is a basic source of
information for investment and other related
decisions. Income measurement has always
been a challenge for accounting standard
setting bodies. In order to assess the future
income and cash flows, investors rely on
income reported. However, the components
of income reported must be presented fairly
and accurately. The cash flow statement,
include important information about cash
flows from various activities. Cash generated
from operating activities and other sources is
consumed for performing operation, paying
dividends, repaying debts, etc. Cash inflows
and outflows in any enterprise is the result of
management decisions related to short-term
and long-term operational plans, financing
and investment plans.
The income statement and cash flow
statement are two means of providing
important information about firm’s
performance.Investorsandotherusergroups
extensively rely on the information that is
disclosed in these two financial statements.
In this research, we investigate the
association strength of the components of
income statement and cash flow statement
with stock returns.
Previous studies
Bernard and Stober (1989) investigate
the nature and amount of information in
cash flows and accruals. They find no
evidence that stock prices respond in a
systematic manner to release of information
about the cash flow and accrual components
of earnings and guess that the information
content of these two components of earnings
may not be systematically different.
Watson and Wells (2005) study the
association between various earnings and
cash flows measures of firm performance
and stock returns in Australian Stock
Exchange. They report that for profit making
firms, earning based performance measures
are found to be more closely associated with
stock returns than cash flow based measures.
However, for loss making firms, they find
that neither earning nor cash flow based
measures capture firm performance well.
Livnat and Santicchia (2006) test the
association between cash flows, accruals
and future returns. They find that, future
quarterlyearningsaremorehighlyassociated
with current net operating cash flows than
with accruals because accruals have less
persistence and companies with extremely
high (low) current quarterly accruals have
significant and negative (positive) abnormal
returns.
Rayburn (1986) investigates the
association of operating cash flow and
accruals with security returns. The results of
his research support the association of both
operating cash flow and aggregate accruals
with abnormal returns.
Dechow (1994) studies the accounting
earnings and cash flows as measures of firm
performance. She finds that, both operating
cash flows and accruals have incremental
information content over each other and
they are priced differently by the market.
Bown et al. (1986) report low correlation
between percentage changes in alternative
measures of cash flow and both percentage
changes in earnings and percentage changes
in traditional cash flow.

2009 11
Sloan (1996) investigates whether stock
prices reflect information about future
earnings contained in the accrual and cash
flow components of current earnings. He
finds that, stock prices do not reflect fully
informationcontainedintheaccrualandcash
flow components of current earnings until
that information impacts future earnings.
Wilson (1986) examines the relative
information content of accruals and cash
flows. He defines funds and accruals as
cash from operations and total accruals,
respectively and repots that these parameters
are both significantly different from zero
and from each other. This result indicates
that these components of earnings have
incremental information content beyond
earningsandbeyondeachother.Inparticular,
the non-cash component of earnings has
incremental information content beyond the
cash component. Wilson (1987) in another
research studies the incremental information
content of the accrual and funds components
of earnings after controlling for earnings and
finds that, at least one of these components
has information content and after controlling
for earnings, incremental information about
the cash and non-cash components of
earning is precisely the same.
Haw et al. (2001) examine the nature of
information in accruals and cash flows in
an emerging capital market. Their results
demonstrate that earnings have relative
information content over operating cash
flows and also earnings have greater
persistence and predictability than operating
cash flows.
Sharma and Iselin (2003) investigate the
decision usefulness of reported cash flow
and accrual information. They find that,
judgments based on cash flow information
are more accurate than judgments based on
accrual information and the difference in
judgment accuracy is more pronounced for
insolvent (failed) companies than for solvent
(non-failed) companies. Sharma and Iselin
(2003) in another research also investigate
the relative relevance of cash flow and
accrual information solvency assessments
and find that, relative to accrual information,
cash flow information enhances the
accuracy of solvency assessments and cash
flow information had greater relevance than
accrual information for solvency judgments.
Livnat and Zarowin (1990) survey the
incremental information content of cash
flow components. They find that there is
no incremental information content of cash
flows beyond net income. However, they
show that the association of cash flows with
stock returns increases when earnings are
disaggregated into components of cash flows
from financing, investing and operating
activities and accruals.
Dastgir and Saeedi (2006) study the
superiority of comprehensive income to net
income as a measure of firm performance.
They find that, that comprehensive income
is not superior to net income for evaluating
firm performance on the basis of stock
return and price. For the state companies,
they find that, firm performance evaluation
on the basis of cash flows prediction using
comprehensive income is superior to net
income.
Further, research findings in Ball and
Brown (1968) and Beaver and Dukes
(1972) indicate that earnings have a higher
association with security returns than cash
flows with security returns.
Variables
Dependent Variable
In this research, the depended variable is
firms’ stock returns (SR). We collected the
required data for this variable using Sahra
Software (the Iranian software).

Independent variables
We use the components of income
statements and components of cash flow
statement as independent variables. We use
the electronic archival data provided by
Tehran Stock Exchange (TSE) to collect
data.
The components which we choose as
components of income statements are Gross
Income per share (GI), Operation Income
per share (OI), Income before Tax per share
(IBT) and Net Income per share (NI).
The components of cash flow statement
in this study are Cash Flows from Operating
Activities per share (OC), Cash Flows from
Investments Returns and Income Payable
for Financing Activities per share (RC),
Cash Flows from Income Tax per share
(TC), Cash Flows from Investing Activities
per share (IC), Total Cash Flows before
Financing Activities per share (CBF) and
Cash Flows from Financing Activities per
share (FC).1
Hypotheses
For studying the association between
components of income statements and
components of cash flow statement with
stock returns, we test the following
hypotheses:
H1
: Among the components of income
statement, operating income has a stronger
relationship with stock returns.
H2
: Among the components of cash flow
statement, the cash flows from operating
activities have a stronger relationship with
stock returns.
H3
: Components of cash flow statement
have stronger association with stock returns
than components of income statement.
Sample
The sample of this study is selected based
on the availability of the required data for
the period of 2003 to 2005. From listed
companies on Tehran Stock Exchange, first
those companies having available data and
their year end is 21st March (Iranian fiscal
year end) is selected. Then investment and
brokerage companies are omitted and 65
companies randomly selected for this study.
The sample of 65 companies listed in Tehran
Stock Exchange for the time period of 2003-
2005 is shown in the table 1.
Hypothesis Testing
In this research we test each hypothesis
in four situations. In first situation we use
the pooling data approach for three years
and 195 firm-year observations during
2003-2005. In other three situations we use
the cross-sectional approach for each year
during 2003-2005. In each situation we
estimate various regression models. After
estimating the regressions we compare
the adjusted across the various regression
models.
We tried to use regression models
in each situation that have the same
dependentvariable.
1
These components are based on Iran’s Accounting Standards. Ac-
cording to Iran’s Accounting Standards all firms must disclose
these items in statements of cash flows.

2009 13
Industry Groups
Year 2003 Year 2004 Year 2005 Total Sample
No. % No. % No. % No. %
Metal Mines 2 3.1 2 3.1 2 3.1 6 3.1
Other Mines 2 3.1 2 3.1 2 3.1 6 3.1
Non-metal Mines 10 15.3 9 13.8 10 15.3 29 14.9
Drugs & Chemical 8 12.3 8 12.3 7 10.8 23 11.8
Plants & Equipments 8 12.3 7 10.8 6 9.2 21 10.8
Food 8 12.3 9 13.8 8 12.3 25 12.8
Rubber & Plastic 3 4.6 3 4.6 4 6.2 10 5.1
Oil Products 2 3.1 2 3.1 2 3.1 6 3.1
Textile 3 4.6 4 6.2 5 7.7 12 6.2
Main Metals 4 6.2 4 6.2 5 7.7 13 6.7
Metal Products 2 3.1 3 4.6 3 4.6 8 4.1
Appliances & Electrics 3 4.6 4 6.2 2 3.1 9 4.6
Auto 6 9.2 5 7.7 6 9.2 17 8.6
Others 4 6.2 3 4.6 3 4.6 10 5.1
Total 65 100 65 100 65 100 195 100
H1
Testing
To test the first hypothesis by using the
pooling data, we estimate the following
regression models:
H2
Testing
For testing the second hypothesis by
using the pooling data, we estimate the
following regressions for 2003 to 2005:
Where:
SR is stock returns, GI is gross income
(loss) per share, OI is operating income
(loss) per share, IBT is income (loss) before
tax per share and NI is net income (loss) per
share.
In order to test the first hypothesis by
using the cross-sectional data, we estimate
the following regressions for 2003 to 2005:
2003
( )
SR LOG GI
i i
a b f
= + +
( )
SR LOG OI
i i
a b f
= + +
( )
SR LOG IBT
i i
a b f
= + +
( )
SR LOG NI
i i
a b f
= + +
SR GI
i i
a b f
= + +
SR OI
i i
a b f
= + +
SR IBT
i i
a b f
= + +
SR NI
i i
a b f
= + +
2004
2005
( )
SR LOG GI
i i
a b f
= + +
SR OI
i i
a b f
= + +
( )
SR LOG IBT
i i
a b f
= + +
( )
SR LOG NI
i i
a b f
= + +
( ) ( )
LOG SR LOG GI
i i
a b f
= + +
( ) ( )
LOG SR LOG OI
i i
a b f
= + +
( ) ( )
LOG SR LOG IBT
i i
a b f
= + +
( ) ( )
LOG SR LOG NI
i i
a b f
= + +
SR OC
i i
a b f
= + +
SR RC
i i
a b f
= + +
SR TC
i i
a b f
= + +
SR IC
i i
a b f
= + +
SR CBF
i i
a b f
= + +
SR FC
i i
a b f
= + +
Table 1. A Sample of Companies Listed in Tehran Stock Exchange

Where:
OC is cash flows from operating activities
per share, RC is cash flows from investments
returns and income payable for financing
activities per share , TC is cash flows from
income tax per share , IC is cash flows from
investing activities per share, CBF is total
cash flows before financing activities per
share and FC is cash flows from financing
activities per share.
In order to test the second hypothesis by
2003
2003
2004
2005
2004
2005
( )
SR LOG OC
i i
a b f
= + +
( )
SR LOG RC
i i
a b f
= + +
SR TC
i i
a b f
= + +
( )
SR LOG IC
i i
a b f
= + +
SR CBF
i i
a b f
= + +
SR FC
i i
a b f
= + +
SR OC
i i
a b f
= + +
SR RC
i i
a b f
= + +
SR TC
i i
a b f
= + +
SR IC
i i
a b f
= + +
SR CBF
i i
a b f
= + +
SR FC
i i
a b f
= + +
( ) ( )
LOG SR LOG OC
i i
a b f
= + +
( )
LOG SR RC
i i
a b f
= + +
( )
LOG SR TC
i i
a b f
= + +
( ) ( )
LOG SR LOG IC
i i
a b f
= + +
( ) ( )
LOG SR LOG CBF
i i
a b f
= + +
( )
LOG SR FC
i i
a b f
= + +
H3
Testing
In order to test the third hypothesis by
using the pooling data, we estimate the
following regressions:
SR OC RC TC IC
CBF FC
1 2 3 4
5 6
i i i i i
i i
a b b b b
b b f
= + + + +
+ + +
( ) ( )
( )
SR LOG GI OI LOG IBT
LOG NI
3
4
i
i
2 i
1
i i
a b b b
b f
= + + +
+ +
In order to test the third hypothesis by
SR GI OI IBT
NI
1 2 3
4
i i i i
i
a b b b
b f
= + + +
+ +
SR OC RC TC
IC CBF FC
i i i i
i i i
1 2 3
4 5 6
a b b b
b b b f
= + + +
+ + + +
SR GI OI IBT
NI
i i i i
i
1 2 3
4
a b b b
b f
= + + +
+ +
( )
( ) ( )
SR LOG GI OI
LOG IBT LOG NI
i i i
i i
1 2
3 4
a b b
b b f
= + +
+ + +
( )
SR LOG OC RC TC
IC CBF FC
i i i i
i i i
1 2 3
4 5 6
a b b b
b b b f
= + + +
+ + + +
SR OC RC TC
IC CBF FC
i i i i
i i i
1 2 3
4 5 6
a b b b
b b b f
= + + +
+ + + +
The Results of Hypotheses
Testing
In this section we present the findings of
testing research hypotheses. The following

2009 15
subsections provide the results of hypotheses
testing in four situations.
A) First Hypothesis
a) The results of testing by using the
pooling data approach
The results of the estimating the models
related to this hypothesis are shown in table
(2). As shown in the table, the P-values of
all T-statistics of all variables are significant.
The P-values of all F-statistics for all models
are significant too and show that, all models
are significant in general. The adjusted of
models are 0.198, 0.290, 0.289 and 0.344
respectively. By comparison of adjusted of
models, it is obvious that Net Income (Loss)
variable has a stronger relationship with
stock returns.
However, the results of estimating these
models do not show that, among components
of income statements, operation income
(loss) has a stronger relationship with stock
returns.
Table 2. The results of H1
testing by using the poling
data approach.
testing by using the cross-
sectional approach-year 2003
Variable
F-statistic
(Prob)
T-statistic
(Prob)
Coefficient
C
(Prob)
DW
GI 0.198 48.951
(0.000)
5.280
(0.000)
0.011 18.097
(0.000)
1.980
OI 0.290 80.173
(0.000)
8.198
(0.000)
0.015 20.054
(0.000)
2.004
IBT 0.289 79.794
(0.000)
6.911
(0.000)
0.014 22.090
(0.000)
1.981
NI 0.344 102.551
(0.000)
8.027
(0.000)
0.017 22.437
(0.000)
1.977
R
2
b) The results of testing by using the
cross-sectional approach
related to this hypothesis for year 2003 are
shown in table (3). As shown in the table,
except the GI variable, the P-value of all
T-statistics of all variables are significant.
The P-values of F-statistics for all models,
except the GI variable, are significant too
and show that, these models are significant
in general. The adjusted R2
of models are
0.036, 0.153, 0.093 and 0.100 respectively.
By comparison of adjusted R2
of models,
it is obvious that Operation Income (Loss)
variable has a stronger relationship with
stock returns.
models show that, among components
returns.
Variable
F-statistic
(Prob)
T-statistic
(Prob)
Coefficient
C
(Prob)
DW
LOG
(GI)
0.036 3.307
(0.074)
1.819
(0.074)
25.874 -125.749
(0.225)
1.928
LOG
(OI)
0.153 10.965
(0.002)
3.311
(0.002)
44.105 -251.960
(0.009)
2.083
LOG
(IBT)
0.093 6.663
(0.013)
2.581
(0.013)
39.460 -146.039
(0.083)
1.873
LOG
(NI)
0.100 7.125
(0.010)
2.669
(0.010)
31.405 -147.347
(0.073)
1.854
R
2
related to first hypothesis for year 2004
are shown in table (4). As shown in the
table, the P-values of all T-statistics of all
variables are significant. The P-values of all
F-statistics for all models are significant too
and show that, these models are significant
of models are
of models, it is
obvious that Net Income (Loss) variable has
a stronger relationship with stock returns.
of income statement, operation income

returns.
Variable
F-statistic
(Prob)
T-statistic
(Prob)
Coefficient
C
(Prob)
DW
LOG
(GI)
0.049 3.945
(0.051)
2.663
(0.010)
15.646 -68.875
(0.078)
1.818
OI 0.079 6.501
(0.013)
2.550
(0.013)
0.013 25.935
(0.003)
1.810
LOG
(IBT)
0.164 10.188
(0.003)
3.463
(0.001)
26.058 -130.114
(0.008)
1.640
LOG
(NI)
0.172 10.954
(0.002)
3.511
(0.001)
26.393 -127.840
(0.007)
1.602
R
2
related to first hypothesis for year 2005 are
shown in table (5).As shown in the table, the
P-values of all T-statistics of all variables are
significant. The P-values of all F-statistics
of all models are significant too and show
that, these models are significant in general.
The adjusted of R2
models are 0.123, 0.089,
0.085 and 0.073 respectively. By comparison
of adjusted of R2
models, it is obvious that
Gross Income (Loss) variable has a stronger
returns.
testing by using the
pooling data approach
Variable
F-statistic
(Prob)
T-statistic
(Prob)
Coefficient
C
(Prob)
DW
LOG
(GI)
0.123 6.872
(0.012)
-6.583
(0.000)
-0.287 5.865
(0.000)
2.351
LOG
(OI)
0.089 5.021
(0.031)
-4.679
(0.000)
-0.265 5.624
(0.000)
2.399
LOG
(IBT)
0.085 4.819
(0.034)
-3.740
(0.000)
-0.245 5.454
(0.000)
2.480
LOG
(NI)
0.073 4.211
(0.047)
-3.219
(0.003)
-0.236 5.352
(0.000)
2.467
R
2
B) Second Hypothesis
a) The results of H2
poling data approach
related to this hypothesis are shown in table
(6). As shown in the table, the P-values of
T-statistics of OC, RC and IC variables are
significant. The P-values of all F-statistics
for all models are significant too and show
that, all models are significant in general.
The adjusted R2
of models are 0.104, 0.107,
of models, it
is obvious that cash flows from investing
activities (IC) variable have a stronger
of statements of cash flows, the cash flows
from operating activities have a stronger
Variable
F-statistic
(Prob)
T-statistic
(Prob)
Coefficient
C
(Prob)
DW
OC 0.104 23.424
(0.000)
4.406
(0.000)
0.005 31.049
(0.000)
1.899
RC 0.107 24.357
(0.000)
-2.990
(0.003)
-0.014 27.454
(0.000)
1.876
TC 0.075 16.773
(0.000)
-1.147
(0.253)
-0.007 33.649
(0.000)
1.889
IC 0.149 34.907
(0.000)
-2.647
(0.009)
-0.012 31.114
(0.000)
1.941
CBF 0.121 27.598
(0.000)
-0.908
(0.365)
-0.002 35.718
(0.000)
1.870
FC 0.109 24.742
(0.000)
0.191
(0.849)
0.001 35.898
(0.000)
1.866
R
2
b) The results of H2
cross-sectional approach
related to this hypothesis for year 2003

2009 17
Variable
F-statistic
(Prob)
T-statistic
(Prob)
Coefficient
C
(Prob)
DW
Log
(OC)
0.189
11.951
(0.001)
3.457
(0.001) 50.539
-292.149
(0.006)
1.919
Log
(RC)
0.095
1.824
(0.217)
1.357
(0.217)
15.857
-12.494
(0.848)
2.079
TC 0.006
0.346
(0.559)
-0.588
(0.559)
-0.021
58.677
(0.002)
1.778
Log
(IC)
0.070
2.423
(0.137)
1.557
(0.137)
23.081
-83.278
(0.356)
0.756
CBF 0.008
1.490
(0.227)
-1.221
(0.227)
-0.015
56.161
(0.001)
1.765
FC 0.029
2.886
(0.094)
1.699
(0.094)
0.022
50.239
(0.006)
1.780
Variable
F-statistic.
(Prob)
T-statistic
(Prob)
Coefficient
C
(Prob)
DW
OC 0.107
8.709
(0.004)
2.721
(0.008)
0.016
23.868
(0.000)
1.848
RC 0.009
0.948
(0.334)
-0.980
(0.331)
-0.011
30.990
(0.001)
1.696
TC 0.006
0.637
(0.428)
-1.045
(0.300)
-0.019
33.466
(0.000)
1.675
IC 0.009
0.227
(0.636)
-0.478
(0.434)
-0.003
36.395
(0.000)
1.671
CBF 0.011
1.705
(0.196)
1.774
(0.081)
0.006
41.404
(0.000)
1.755
FC 0.005
1.351
(0.249)
-1.705
(0.093)
-0.005
40.915
(0.000)
1.738
R
2
R
2
table, only the P-value of T-statistic of
OC is significant and only the P-value of
F-statistic for this model is significant and
shows that, only this model is significant
of models are
0.189, 0.095, 0.006, 0.070, 0.008 and 0.029
respectively. By comparison of adjusted R2
of models, it is obvious that cash flows from
operating activities variable have a stronger
of cash flow statement, the cash flows
related to second hypothesis for year 2004
OC is significant and only the P-value of
of models are
0.107, 0.009, 0.006, 0.009, 0.011 and 0.005
operating activities variable have a stronger
related to second hypothesis for year 2005
CBF is significant and only the P-value of
of models are
0.064, 0.009, 0.009, 0.729, 0.498 and 0.010
investing activities variable have a stronger

Variable
F-statistic.
(Prob)
T-statistic
(Prob)
Coefficient
C
(Prob)
DW
Log
(OC)
0.064
3.672
(0.063)
-1.916
(0.063)
-0.194
5.081
(0.000)
2.398
RC 0.009
1.384
(0.246)
1.176
(0.246)
0.001
3.999
(0.001)
2.366
TC 0.009
1.373
(0.248)
1.522
(0.135)
0.001
3.938
(0.000)
2.326
Log
(IC)
0.729
9.058
(0.095)
-4.131
(0.054)
-0.752
7.790
(0.000)
0.435
Log
(CBF)
0.498
14.878
(0.002)
-4.531
(0.001)
-0.419
6.307
(0.000)
1.205
FC 0.010
0.561
(0.458)
0.811
(0.422)
0.001
3.701
(0.000)
2.266
R
2
C) Third Hypothesis
Inordertotestthishypothesisweestimate
two regression models. The first model is
related to components of income statements
and second is related to components of cash
flow statement.
a) The results of testing by using the
poling data approach
The results of estimating the models
related to this hypothesis are shown in first
pair-columns in table (10).As shown in table,
in first model, the P-values of T-statistics of
all coefficients are significant. In second
model, except , the P-values of T-statistics
of all coefficients are significant too. The
F-statistics related to both models show that
the models are significant in general. The
adjusted R2
of models are 0.759 and 0.192
respectively. However, this result shows
that, relative to components of cash flow
statement, there is a stronger association
between stock returns and components of
income statements.
related to this hypothesis for year 2003
are shown in second pair-columns in table
(10). As shown in table(10), in first model,
6
b
the P-values of T-statistics of and are
significant. In second model, the P-values
of T-statistics of all coefficients are not
significant. The F-statistics related to
both models show that the models are not
significant in general. The adjusted R2
of
models are 0.077 and 0.024 respectively.
However, this result shows that, relative to
components of cash flow statements, there is
a stronger association between stock returns
and components of income statements.
related to third hypothesis for year 2004 are
shown in third pair-columns in table (10).
As shown in table, in first model, only the
P-value of T-statistic of is significant. In
second model, the P-values of T-statistics
of all coefficients are not significant. The
F-statistics related to both models show that
only the first model is significant in general.
The adjusted R2
of models are 0.123 and
0.100 respectively. However, this result
shows that, Relative to components of cash
flow statement, there is a stronger association
between stock returns and components of
income statements.
related to third hypothesis for year 2005 are
shown in fourth pair-columns in table (10).
Asshownintable,infirstmodel,theP-values
of T-statistics of and are significant. In
second model, only the P-value of T-statistic
of is significant. The F-statistics related to
both models show that only the first model
is significant in general. The adjusted R2
of
models are 0.156 and 0.067 respectively.
However, this result shows that, relative to
components of statements of cash flows,
there is a stronger association between
stock returns and components of income
statements.
3
b 4
b
1
b
1
b 2
b
1
b

2009 19
Pooling Data
Cross-sectional
2003
Cross-sectional
2004
Cross-sectional 2005
1st
model
2nd
model
1st
model
2nd
model
1st
model
2nd
model
1st
model
2nd
model
R
2
0.759 0.192 0.077 0.024 0.123 0.100 0.156 0.067
F-statistic
(Prob)
125.448
(0.000)
8.661
(0.000)
2.340
(0.065)
1.267
(0.287)
3.241
(0.018)
2.179
(0.058)
3.497
(0.014)
1.604
(0.103)
1
b
T-statistic
(Prob)
-48.713
-10.571
(0.000)
-894.68
-2.497
(0.013)
0.035
1.214
(0.230)
-956.22
-0.412
(0.682)
-0.020
-0.237
(0.029)
-1036.6
-0.863
(0.392)
-42.302
-2.812
(0.007)
-36.386
-2.562
(0.014)
2
b
T-statistic
(Prob)
0.032
18.798
(0.000)
-894.69
-2.497
(0.013)
-0.080
-1.359
(0.179)
-956.27
-0.412
(0.682)
0.005
0.204
(0.839)
-1036.6
-0.863
(0.392)
0.032
3.368
(0.001)
0.028
0.765
(0.448)
3
b
T-statistic
(Prob)
-97.539
-4.741
(0.000)
-894.66
-2.497
(0.013)
0.241
2.349
(0.022)
-956.20
-0.412
(0.682)
-0.013
-0.195
(0.846)
-1036.6
-0.863
(0.392)
-311.83
-1.875
(0.067)
-0.069
-1.236
(0.223)
4
b
T-statistic
(Prob)
111.510
5.312
(0.000)
-894.69
-2.497
(0.013)
-0.217
-0.162
(0.035)
-956.23
-0.412
(0.682)
0.044
0.684
(0.497)
-1036.7
-0.863
(0.392)
316.603
1.918
(0.061)
-0.024
-0.918
(0.363)
5
b
T-statistic
(Prob)
-
894.672
2.497
(0.013)
-
956.26
0.412
(0.682)
-
1036.7
0.863
(0.391)
-
0.004
0.235
(0.815)
6
b
T-statistic
(Prob)
-
-0.007
-0.962
(0.337)
-
0.059
1.519
(0.134)
-
0.033
1.416
(0.162)
-
-0.002
-0.048
(0.962)
C
(Prob)
276.320
(0.000)
26.354
(0.000)
30.005
(0.155)
22.042
(0.352)
43.007
(0.000)
29.681
(0.001)
334.49
(0.000)
288.72
(0.001)
DW 2.082 1.890 1.975 1.758 1.847 1.984 1.822 2.285
Table 10.The results of H3
testing
Conclusions
In this paper, the association between
components of income statements and
components of cash flow statement and
stock returns has been investigated.
Income statements provide information
about gross income (loss), operation income
(loss), income (loss) before tax and net
income (loss). The results of testing first
hypothesis show that, investors and other
users of financial statements concentrate
more on net income (loss).
Cash flow statement provide information
about cash flows from various activities.
The results of testing second hypothesis
show that, investors and other users of
financial statements have more interest
to information related to cash flows from
investing activities. In other hand, when they
use information in cash flow statement, they
concentrate more on investing activities.
The results of testing third hypothesis
show that, components of income
statements have a stronger association with
stock returns. This result is compatible with
those of Watson and Wells (2005), Haw
et al. (2001), Rayburn (1986), Bown et al.

(1986), Wilson (1986), Ball and Brown
(1968), Beaver and Dukes (1972), Livnat
and Zarowin (1990) and incompatible with
Livnat and Santicchia (2006) and Sharma
and Iselin (2003).
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2009 23
MARKET ANALYSIS OF STUDENT’S ATTITUDES
ABOUT CREDIT CARDS
J.C.Arias (PhD, DBA), Robert Miller
Abstract
The attitudes of students to the use of credit cards is a complex subject, one that when measured
needs to combine both demographic and attitudinal data to provide a complete picture of the topic. For
this specific research project, fifty students, 25 of which were men and 25 women, were interviewed,
and their responses entered into SPSS Version 14 and analyzed using frequency distributions and cross-
tabulations. The result is a report that provides a fascinating glimpse into the attitudes of students with
regard to credit cards. Highlights from the report include the following:
• The marketing messages from credit card companies are being very effective in pushing emergency
uses of credit cards as the rationalization for giving one to a student away at school. What follows
however is spending on many other items apart from those that would be considered necessary
for an emergency.
• Women, in general, understand credit and know the interest rates of their credit cards with much
greater frequency than men.
• The need to feel in control and the need to have their egos gratified are two of the strongest
reasons why students continue to accumulate credit cards. The higher the balance for graduate
students the greater the feeling of control.
• Tracking expenses online is split between those students with 3 cards and those with 7 or more.
• 14% of students in the sample have a credit card due to their parents’ thinking they need one for
emergencies, yet have their parents paying a monthly bill at the same time.
• The majority of students feel that credit cards are Ok to be used for meeting daily living expenses
and making ends meet.
Arias J.C., Miller R. - Market Analysis of Student’s Attitudes about Credit Cards

Research Issue
Ascertaining the attitudes of students
relative to credit cards, including their
attitudes about their perceived convenience,
risk, and potential make transactions cost
more than they would if paid in cash were
several of the major attitudinal areas studied
in this survey. Demographics including
both the students’ income and their families’
income are included in the analysis, the
frequency of how often their parents fought
about money in general and credit cards
specifically, and the number of credit cards
they are carrying today were also included in
the analysis. What emerged is a dichotomy
in the views of students on credit cards and
their relative usefulness and risk.
Research Goals
In completing this survey and resulting
analysis, the following research goals were
first defined:
1. Discover through the use of fifteen
attitudinal questions and an additional
twelve demographic variables if there
is any correlation between student’s
attitudes to credit card use and awareness
of the mechanics of how credit works.
Specifically this first goal looks to find if
there is a correlation between students’
lack of knowing the interest rates on
their credit cards relative to who pays
their credit card bill.
2. The relationship between years in school
and the perception of credit cards as a
selective and not all-inclusive spending
resource.
3. Measure students’ attitudes to using
credit cards to feel better about
themselves, specifically more in control
of their lives and feeling more important
or privileged when they get a credit card.
4. Measure student’s attitudes about using
their credit card balances to finance
a vacation or down payment on a car
versus saving credit cards balances for
emergencies.
5. Define what percentage of students in
the sample have at least one credit card
maxed out to its limit and correlate this
to their age and income level.
6. Define the overall attitudes of students
when it comes to credit cards as a
convenience or necessary evil in society.
Methodology
Fifty students were given the printed
questionnaire and assured complete
anonymity and privacy, and also were left
alone in classrooms after sessions were over
to complete the survey. A $3 Starbucks
Card was offered to the first ten students
to complete the survey, so that motivation
to quickly finish the research instrument
would be assured. Graduate-level students
were asked to complete the survey during an
evening course break.
The sampling focused primarily on
business students, with an even mix of
women and men in the samples to rule out
gender bias in the analysis of the results,
a research design advocated by Hair, J.F.,
Anderson, R.E., Tatham, R.L., & Black,
W.C. (1995) in their book.
Simple Random Sampling was used
in the administering of the questionnaires
themselves, and anonymity was assured by
having a box at the front of the room where
the students could place their responses
before leaving class.

2009 25
The questionnaire itself includes 24
questions, with 12 being focused on
demographics and parents’ behavior
around credit cards, and the remaining 12
being focused on attitudinal variables. An
interval-scale questionnaire was created
to capture their attitudes to the following
questions. Each of these questions were
responded to on a four-point scale comprised
of Strongly Agree, Somewhat Agree,
Somewhat Disagree, and Strongly Disagree.
The following attitudinal statements were
responded to in the interview process:
• I feel more in control of my life when I
get a new credit card with a high balance
• I feel important when I apply and get
any credit card
• Credit cards are necessary in today’s
society and provide a needed service
• It’s easy to overspend when you have a
credit card
• Credit cards end up costing me more
than I think
• Credit cards make my spending more
convenient
• One of the big benefits of spending using
a credit card is tracking expenses online
• Right now one or more of my credit cards
are at their maximum limit
• Credit cards are great for establishing
credit
• Credit Cards are risk-free from identity
theft
• Credit Cards should only be used for
emergencies.
• It is OK to charge a vacation entirely on
a credit card
SPSS Version 14.0 for Windows was used
for completing the statistical and graphical
analysis of results, with the data being
input into the Data View and the variables
organized in the Variable View.
Analysis of Results
Starting with the research design, the role
of sex of respondents were held constant to
ensure that this variable would not have to be
controlled for in the analysis. The analysis
suggests that women students, in general,
are more aware of how credit works and its
ramifications on their lives going forward.
Sex of Respondents
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Male 25 50.0 50.0 50.0
Female 25 50.0 50.0 100.0
Total 50 100.0 100.0
Sex of respondent
What’s fascinating about the intelligence
of women in the sample relative to men is
their higher level of awareness of how credit
works. See the cross-tabulation below of
sex of respondent by awareness of interest
rates on credit cards:

Know The Interest Rate
on Cards Total
Yes No
Sex of respondent Male Count 13 12 25
% within Sex of respondent 52.0% 48.0% 100.0%
% within Know The Interest Rate on Cards 38.2% 75.0% 50.0%
% of Total 26.0% 24.0% 50.0%
Female Count 21 4 25
% of Total 42.0% 8.0% 50.0%
Total Count 34 16 50
% of Total 68.0% 32.0% 100.0%
Sex of respondent * Know The Interest Rate on Cards Cross tabulation
Female
Male
Sex of respondent
25
20
15
10
5
0
Count
Comparing Men's and Women's Awareness of Interest Rates on their credit
cards
No
Yes
Know The Interest Rate
on Cards
Clearly women understand the
implications of credit card debt before and
at a much more fundamental level than
men in this sample as the chart, Comparing
Men’s & Women’s Awareness of Interest
Rates on their credit cards which is shown
in the graphic to the left
Attitudes towards Credit Cards
The main research objective of this paper
is to define student’s attitudes’ about credit
cards. From the research completed by
Hayhoe, Leach, Allen, and Edwards (2005)
the researchers found that students acquire
and spend more to feel more in control over
their lives. 36% respondents agree with
the hypothesis of the researchers mentioned,

2009 27
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Strongly
Agree
18 36.0 36.0 36.0
Somewhat
Agree
28 56.0 56.0 92.0
Somewhat
disagree
4 8.0 8.0 100.0
Total 50 100.0 100.0
Credit Cards Make Me Feel In Control
When this attitudinal variable of feeling
in control is cross-tabulated by the class
rank of the respondent, another fascinating
dynamic emerges, showing that the higher
theclassrankthemoreincontrolrespondents
feel about their use of credit cards. This
is attributed to the fact that in general, the
higher the class rank the higher the credit
limits, and the greater the opportunities to
make good and bad decisions in the use of
credit cards. The following table shows
a cross-tabulation of class rank by the
attitudinal variable of credit cards making
the respondent feel in control. Notice
that not a single respondent completely
disagreed with this attitudinal statement – a
sure sign being in control is correlated with
a high credit limit.
CC Makes Me Feel In Control
Total
Strongly
Agree
Somewhat
Agree
Somewhat
disagree
Class in
School
Freshman Count 1 3 0 4
% within Class in School 25.0% 75.0% .0% 100.0%
% within CC Makes Me Feel In Control 5.6% 10.7% .0% 8.0%
% of Total 2.0% 6.0% .0% 8.0%
Sophomore Count 0 3 0 3
% within Class in School .0% 100.0% .0% 100.0%
% within CC Makes Me Feel In Control .0% 10.7% .0% 6.0%
% of Total .0% 6.0% .0% 6.0%
Junior Count 2 4 1 7
% within Class in School 28.6% 57.1% 14.3% 100.0%
% within CC Makes Me Feel In Control 11.1% 14.3% 25.0% 14.0%
% of Total 4.0% 8.0% 2.0% 14.0%
Senior Count 2 4 1 7
% of Total 4.0% 8.0% 2.0% 14.0%
Cross-tabulation of Class in School with attitudinal variable Credit Cards Make Me Feel In Control
in that they strong agree with the statement
that credit cards give them a strong sense of
control.

Attitudes to Credit Cards: The
Marketing of Ego
For many respondents, applying for and
getting a credit card is a big boost to their ego.
In the fifty respondents in this survey, not a
single one said they completely disagreed
with this statement. In fact, many of them
feel that this is the biggest pay-off of going
for even more cards; there is the validation
that they are worthy of someone’s trust with
a credit card, and the freedom it conveys is a
powerful force in acquiring more and more
cards. 58% of respondents strongly agree
with this statement, and an additional 40%
somewhat agree. This is the most powerful
allure of credit cards to students, the feeling
that they “are somebody” when they get a
credit card. One student also wrote in that
getting an American Express card felt better
than getting straight As.
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Strongly
Agree
29 58.0 58.0 58.0
Somewhat
Agree
20 40.0 40.0 98.0
Somewhat
disagree
1 2.0 2.0 100.0
Total 50 100.0 100.0
Feel more important when I get a Credit Card
When a histogram is produced in SPSS
V.14, the results continue to make the point
that the ego gratification of getting a credit
card far outweighs the risks, and for men
students especially, they are more often than
not aware of the interest rate payments are
based on.
Necessary for Society?
When student respondents were asked if
a credit card was essential in westernized
society, 64% strongly agreed, followed by
28% somewhat agreeing. There were no
overt negative responses to this question, as
credit cards have become a fact of life for
many of these students.
3.50
3.00
2.50
2.00
1.50
1.00
0.50
Feel more important when I get one
40
30
20
10
0
Frequency
Mean =1.44
Std. Dev. =0.5406
N =50
Histogram
CC Makes Me Feel In Control
Total
Strongly
Agree
Somewhat
Agree
Somewhat
disagree
Graduate Student Count 13 14 2 29
% of Total 26.0% 28.0% 4.0% 58.0%
Total Count 18 28 4 50
% of Total 36.0% 56.0% 8.0% 100.0%

2009 29
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Strongly Agree 32 64.0 64.0 64.0
Somewhat
Agree
14 28.0 28.0 92.0
Somewhat
disagree
4 8.0 8.0 100.0
Total 50 100.0 100.0
Credit Cards are necessary in society
The histogram analysis from this
specific attitudinal question also shows the
prevalence of how critical students see credit
cards in society. As is the case with other
attitudinal variables that are focused on the
pervasiveness of credit cards, the majority
of students commented that they did not feel
prepared for a semester without at least one
credit card with a low enough balance to be
used during the year.
3.50
3.00
2.50
2.00
1.50
1.00
0.50
CC are necessary in society
40
30
20
10
0
Frequency
Mean =1.44
Std. Dev. =0.64397
N =50
Histogram
Attitudes and Beliefs Regarding
Overspending
The higher the credit limit the more the
attitude prevails that it’s easier to over-
spend on a credit card. The table below that
captures the attitudes of respondents in terms
of their attitudes to over-spending. When
this attitudinal variable is cross-tabulated to
respondent’s class rank, graduate students
have the greatest fear of overspending.
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Somewhat
Agree
17 34.0 34.0 84.0
Somewhat
disagree
8 16.0 16.0 100.0
Total 50 100.0 100.0
Easy to overspend on a Credit Card
When this attitudinal variable was cross-
tabulated to the total number of credit cards
a student has, the median number of 7 total
credit cards held was the delineating point
where students started reporting heavily that
it was easier to overspend with their credit
cards.
3.50
3.00
2.50
2.00
1.50
1.00
0.50
Easy to overspend on a CC
30
25
20
15
10
5
0
Frequency
Mean =1.66
Std. Dev. =0.74533
N =50
Easy to overspend on a CC

Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Somewhat
Agree
17 34.0 34.0 42.0
Somewhat
disagree
18 36.0 36.0 78.0
Strongly
Disagree
11 22.0 22.0 100.0
Total 50 100.0 100.0
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Somewhat
Agree
16 32.0 32.0 94.0
Somewhat
disagree
3 6.0 6.0 100.0
Total 50 100.0 100.0
Credit Cards cost more than I think typically
Credit Cards Makes Spending Convenient
Credit Cards Cost More Than I Think
A common attitude among the broader
consumer population is that credit cards
are more expensive than their initial claims
suggest, including escalating interest rates
and for some cards, an annual renewal
fee that can be in the hundreds of dollars.
As many of the students in the sample
undoubtedly have excellent credit scores
due to little or no debt to this point in their
lives and the fact that credit card companies
are anxious to gain them as new customers,
it’s not surprising to see student’s attitudes
be contrarian. The following table shows
the results of this attitudinal variables’
result, with 22% strongly disagreeing with
the statement that credit cards have higher
costs than they initially thought.
This is the customer segment credit card
companies want most, and their marketing
appears to be working based on this survey.
Credit Cards Making Spending
Convenient
The next attitudinal question, that of
how convenient or not credit cards make
spending, 62% of respondents strongly
agreed with that statement, and no
respondents completely disagreed.
The histogram for this specific variable’
s’hows that overall student respondents
sees the positive aspects of using credit
cards, and given the fact they are in the
primary target market for many of the credit
card companies, it’s again clear to see the
messaging is working. One student also
mentioned that gift credit cards from parents
and relatives were all he asked for during the
last holiday season, and he promptly used
the gift cards to travel to Mexico for Spring
Break.
3.50
3.00
2.50
2.00
1.50
1.00
0.50
CC Makes Spending Convenient
40
30
20
10
0
Frequency
Mean =1.44
Std. Dev. =0.61146
N =50
CC Makes Spending Convenient

2009 31
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Somewhat
Agree
9 18.0 18.0 58.0
Somewhat
disagree
14 28.0 28.0 86.0
Strongly
Disagree
7 14.0 14.0 100.0
Total 50 100.0 100.0
Tracking Expenses Online
Tracking Expenses Online: The
Attitude of Accountability
The focus on accountability from and
validationofspendingbeingtherespondents’
own doesn’t seem to nearly as important
attitudinally as feeling in control and also
getting the ego gratification of getting a
new credit card, which is a point validated
by Davies, E., & Lea, S. E. G. (1995). Only
40% of the respondents strongly agreed
with the point that tracking expenses online
was a task that made holding credit cards
convenient.
The histogram of the attitudinal variable
for tracking expenses online shows the
polarity of how students see this specific
area attitudinally. When this specific
attitudinal variable is cross-tabulated with
the total number of cards a student owns,
which is shown in the table below, shows
the polarity of those student respondents
clustered at the 3 and 7 card areas. The
7-card area specifically is the breakout
area for respondents who also know their
interest rates on cards and have the strongest
attitudes towards being in control with
higher available balances.
4.00
2.00
0.00
TrackingExpensesOnline
20
15
10
5
0
Frequency
Mean =2.16
Std. Dev. =1.11319
N =50
TrackingExpensesOnline
Tracking Expenses Online
Total
Strongly
Agree
Somewhat
Agree
Somewhat
disagree
Strongly
Disagree
Total Number of
Cards
1.00 2.0% 6.0% 2.0% 10.0%
2.00 2.0% 2.0% 6.0% 6.0% 16.0%
3.00 10.0% 4.0% 6.0% 20.0%
4.00 2.0% 4.0% 6.0% 12.0%
5.00 4.0% 6.0% 2.0% 12.0%
6.00 6.0% 6.0%
7.00 10.0% 2.0% 4.0% 16.0%
8.00 6.0% 6.0%
10.00 2.0% 2.0%
Total 40.0% 18.0% 28.0% 14.0% 100.0%
Total Number of Cards by Tracking Expenses Online Cross-tabulation
% of Total

Establishing Credit: Rationalization
or Planning for the Future?
Respondents felt mixed about credit
cards establishing their credit. 36% agreed
with this and had a positive attitude towards
this statement, while 30% disagreed and
in conversations on this general topic, felt
getting a home was by far more important
to their credit standing than getting a credit
card.
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Somewhat
Agree
17 34.0 34.0 70.0
Somewhat
disagree
11 22.0 22.0 92.0
Strongly
Disagree
4 8.0 8.0 100.0
Total 50 100.0 100.0
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Somewhat
Agree
11 22.0 22.0 60.0
Somewhat
disagree
12 24.0 24.0 84.0
Strongly
Disagree
8 16.0 16.0 100.0
Total 50 100.0 100.0
Credits Are Critical for Establishing Credit
One or more credit cards are maxed out
4.00
2.00
0.00
Establishing Credit
25
20
15
10
5
0
Frequency
Mean =2.02
Std. Dev. =0.9581
N =50
Establishing Credit
The histogram graphically shows the
fact that many respondents feel that this is
a positive aspect of getting a credit card,
yet just as many are in the most negative
attitudinal category, which were not listed
on other variables.
The histogram of responses for this
specific attitudinal variable shows the split
nature of respondents. On the one hand they
have maxed out at least one credit card, yet
on the other it is seen as not that important,
as the split of the histogram shows.
Credit Cards Maxed Out
The attitudes of respondents specifically
on credit cards are also very much influenced
by how maxed out their credit cards are.
60% of the respondents have maxed out one
credit card today.
4.00
2.00
0.00
20
15
10
5
0
Frequency
Mean =2.18
Std. Dev. =1.11922
N =50

2009 33
4.00
2.00
0.00
OK to Charge a Vacation
25
20
15
10
5
0
Frequency
Mean =2.74
Std. Dev. =0.94351
N =50
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Somewhat
Agree
15 30.0 30.0 40.0
Somewhat
disagree
18 36.0 36.0 76.0
Strongly
Disagree
12 24.0 24.0 100.0
Total 50 100.0 100.0
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Somewhat
Agree
15 30.0 30.0 58.0
Somewhat
disagree
13 26.0 26.0 84.0
Strongly
Disagree
8 16.0 16.0 100.0
Total 50 100.0 100.0
Only Used for Emergencies
The histogram for this specific attitudinal
variable also shows that the majority of
respondents disagree with the point of
paying for a vacation using credit cards.
From the histogram however it’s easy to
see how effective the marketing programs
of credit card companies are in persuading
both students and their families that while
emergencyusesofcardsisanoblegoal,many
of these same students have high balances
and many different cards. Clearly they are
more sophisticated at juggling multiple
cards and debts than their parents realize,
a finding also validated by Churaman, C.
V. (1988). The following table shows how
students perceive the emergency nature
of their credit cards by how they pay their
bill, including the dynamic of their parents
paying their credit card bill for them.
14% of respondents have the best of both
Using Credit Cards for Vacations
Respondents feel that in general credit
cards are not a good idea as a means to
financing a vacation. 60% of them say
that they disagree with the specific idea of
using credit cards to pay for a vacation. The
assumption behind this is that a vacation
costing thousands of dollars is not acceptable
as an expense subsidized completely on
credit cards.
Used for Emergencies
One of the greatest marketing messages
credit card companies use is to sell the
concept of getting your children credit cards
for emergencies. Too often the emergencies
are pizza at midnight, a new stereo when
one is broken, or even a new laptop. The
marketing messages to students and their
families however are working when one
looks at this attitudinal variable, with 58%
agreeing with this assessment.

worlds, their parents pay the bill and it is
substantiated with the attitude that it is for
emergencies only.
4.00
2.00
0.00
20
15
10
5
0
Frequency
Mean =2.30
Std. Dev. =1.05463
N =50
Total
Strongly
Agree
Somewhat
Agree
Somewhat
disagree
Strongly
Disagree
How Credit Cards
Are Paid
Online 12.0% 10.0% 18.0% 10.0% 50.0%
By Mail 2.0% 16.0% 8.0% 6.0% 32.0%
Parents Pay Bill 14.0% 4.0% 18.0%
Total 28.0% 30.0% 26.0% 16.0% 100.0%
How Credit Cards Are Paid By Only Used for Emergencies Cross tabulation
% of Total
Making Ends Meet with a Credit
Card: Attitudes towards Everyday
Spending
For many students their first experiences
with budgeting are when they go away
to school. This next attitudinal variable
defines how students feel about credit cards
being used to make ends meet. 56% agree
that this is OK to do with a credit card, while
44% don’t.
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Somewhat
Agree
19 38.0 38.0 56.0
Somewhat
disagree
17 34.0 34.0 90.0
Strongly
Disagree
5 10.0 10.0 100.0
Total 50 100.0 100.0
OK to Make Ends Meet
The histogram for this specific variable
shows that many students actually do use
their credit cards to help make ends meet,
and some feel guilty about it, while others
find this perfectly fine. 14% of these
respondents have parents paying the bill, so
“dressing up” circumstances can keep the
parent paying connection moving.
4.00
2.00
0.00
25
20
15
10
5
0
Frequency
Mean =2.36
Std. Dev. =0.89807
N =50

2009 35
Frecuency
Percent
Valid
Percent
Cumulative
Percent
Valid
Somewhat
Agree
12 24.0 24.0 44.0
Somewhat
disagree
22 44.0 44.0 88.0
Strongly
Disagree
6 12.0 12.0 100.0
Total 50 100.0 100.0
Free of identity theft
Attitudes and Perceptions of Risk
What’s refreshing in this attitude
survey is that 56% of all respondents are
concerned about identity theft from using
their credit cards, and 44% aren’t. Despite
this lack of trust in identity protection credit
cards continue being used heavily in the
respondent population.
The histogram for this specific variable
shows that the majority of respondents have
a decidedly pessimistic view of security
online for their credit cards
4.00
2.00
0.00
25
20
15
10
5
0
Frequency
Mean =2.48
Std. Dev. =0.95276
N =50
Conclusions/Recommendations
The conclusion from this research is that
the marketing of credit cards for college
students, both to their parents and to the
students themselves, is extremely effective.
While not measured to statistical relevance,
it is clear that to a high level of confidence
the messages of control, ego gratification
and the rationalization of emergencies is
working very well.
Recommendations based on this research
are as follows:
1. Greater education into the FICO score
definition based on the use of credit
cards. The fact that so many students
don’t really see credit cards impacting
their credit score in the short-term.
2. Focusing on the number of credit cards
and their interest rates through greater
education is also critical, especially for
men students. There is a big gap in
how many men know the interest rates
they are paying on their credit cards for
example.
3. Defining the costs of carrying multiple
credit cards is also critical. Many
students in the sample believe that there
is a lower cost to managing their credit
cards than the general public believes.
4. The level of accountability for matching
receipts to actual spending is troubling,
as only upper division and graduate-level
students use online services to check the
validity of charges on their account.
Limitations
There are several limitations to this
analysis, and these include the following:

1. There needs to be a casual model
developed that speaks to the marketing
influences on students and their lack of
accountability of how they use credit
cards.
2. Further research into the impact on
FICO and credit scores based on having
multiple credit cards in college.
3. The impact of having seven or more
credit cards for any student and their
future credit score.
References
Hayhoe, Leach, Allen, and Edwards
(2005),Credit Cards Held By College
Students. 2005,Association for Financial
Counseling and Planning Education
Journal.
Churaman, C. V. (1988). College student use
of consumer credit. Proceedings of the
American Council on Consumer Interests
(Ed. by V. Hampton), pp 107-113. ACCI.
Columbia, Mo.
Davies, E., & Lea, S. E. G. (1995). Student
attitudes to student debt. Journal of
Economic Psychology 16, 663-679.
Hair, J.F., Anderson, R.E., Tatham, R.L., &
Black, W.C. (1995). Multivariate data
analysis (4th ed.). Englewood Cliffs, NJ:
Prentice Hall.

2009 37
CUSTOMER EXPERIENCE MANAGEMENT IN
RETAILING
Kamaladevi B.
Abstract
Survival of fittest & fastest is the mantra of today’s business game. To compete successfully in this
business era, the retailer must focus on the customer’s buying experience. To manage a customer’s
experience, retailers should understand what “customer experience” actually means. Customer
Experience Management is a strategy that focuses the operations and processes of a business around
the needs of the individual customer. It represents a strategy that results in a win–win value exchange
between the retailer and its customers. The goal of customer experience management is to move
customers from satisfied to loyal and then from loyal to advocate.This paper focuses on the role of
macro factors in the retail environment and how they can shape customer experiences and behaviors.
Several ways (e.g., Brand, Price, Promotion, Supply Chain Management, Location, Advertising,
Packaging & labeling, Service Mix, and Atmosphere) to deliver a superior customer experience are
identified which should result in higher customer satisfaction, more frequent shopping visits, larger
wallet shares, and higher profits.
Kamaladevi B. - Customer Experience Management in Retailing
Kamaladevi B.

Introduction
Just when companies are becoming
comfortable with the idea of Customer
Relationship Management (CRM), a new
term has emerged: Customer Experience
Management (CEM). The two are similar
in many ways, not least in that they are
both difficult to define. Neither can be
identified with a unique product or a specific
technology; rather, they both comprise a
group of applications, technologies and
analytics that orbit around a central premise.
The premises of CRM and CEM are quite
different, however, and are best understood
when compared side by side.
The idea at the center of CRM can
be stated in the following way: Every
time a company and a customer interact,
the company learns something about
the customer. By capturing, sharing,
analyzing and acting upon this information,
companies can better manage individual
customer profitability. Customer Experience
Management’s premise is almost the mirror
image. It says that every time a company
and a customer interact, the customer learns
something about the company. Depending
upon what is learned from each experience,
customers may alter their behavior in ways
that affect their individual profitability.Thus,
by managing these experiences, companies
can orchestrate more profitable relationships
with their customers.
In a sense, this is a classic nature vs.
nurture argument. CRM uses profiling,
micro-segmentation and predictive analyses
to identify each customer’s figurative genetic
structure.CRMthusuncoversthepreferences
and propensities of customers so that they
can be nudged towards optimal profitability.
Customer Experience Management, on the
other hand, looks at the environment. It
gathers and analyzes information about the
dynamics of interactions between companies
and customers. This information is feed back
to the company in a self-calibrating system
that (in theory) makes optimal use of every
opportunity to influence customer behavior.
Obviously these are overlapping
approaches, and both have merit if designed
and applied intelligently. Up until now
the spotlight has predominantly been on
CRM, in part because it is technologically
impressive (as well as astonishingly
expensive). Unfortunately, CRM has not
been nearly as effective as promised;
according to some estimates, from 50% to
70% of CRM initiatives fail to achieve their
goals.
As CRM is more widely used, its
weaknesses become more apparent.
Analysts have become fond of noting that
there is no R in CRM (some go so far as
to say there is no C, either). The idea of
a “relationship” with customers seems
hollow: CRM is very good at receiving, but
not very good at giving. It asks customers
to provide access and information without
telling them what they will get in return. It
pigeonholes customers based on past actions
without informing them how to build a more
advantageous profile. It prompts customers
to become more valuable to the company
without promising greater value from the
company.
Furthermore, while CRM is fairly
effective at measuring its own successes, it
does not provide much information about
its failures. It can record when customers
respond positively to its automated
prompting and prodding, but it doesn’t
give much insight when customers do not
respond in the predicted way. CRM is thus
unable to determine whether failures are
the result of faulty assumptions, incorrect
information or poor execution. It is also
unable to tell how these “failed” interactions
affect the customer relationship; it treats all
failures as neutral, when in fact the fabric

2009 39
of the relationship may have been weakened
or undermined by a poorly executed service
encounter.
CEM’s strengths lie in precisely the areas
where CRM is weak. By focusing on the
experiences of customers and how those
experiences affect behavior, CEM examines
both the quality of the company’s execution
and the efficiency of the result. It aligns
customer needs with the company’s ability
to fulfill those needs, leading to business
relationships that are mutually beneficial and
that both parties — company and customer
— are motivated to improve.
Examples of CEM
* “Best New Airline of the Year Award
2005” – Kingfisher airlines
Given by Centre for Asia Pacific Aviation
for its significant innovation and outstanding
customer experience.
For the first time in the Indian skies,
Kingfisher Airlines offers world-class in-
flight entertainment with personal video
screens for every seat. There’s a wide
selection of 5 video channels and 10 audio
channels available on- board. Also on offer
are extra-wide seats and spacious legroom,
delicious gourmet meals, international-class
cabin crew and a whole host of comforts and
delights. Kingfisher Airlines also facilitates
doorstep delivery of tickets on guest request.
* Blue Dart Express Limited, South
Asia’s largest integrated air express,
courier and logistics company
Their focus was on providing customers
with quality service and an enhanced
customer experience, they continued to
upgrade and expand their infrastructure, by
adding new facilities in Lucknow, Mumbai,
Pune, Ahmedabad, Meerut and Jaipur, and
moving to a new, state-of-the- art warehouse
facility in Delhi.
* Pizza hut
It recognises frequent callers and the
context of their call enabling the customer
to be routed to the agent who can best fulfill
their requirements, whether its a new order,
changes to an existing order or a status
inquiry on an existing order.
Pizza Hut operators can access up-to-date
information on its outlets in the catchment
area, enabling them to select the Pizza Hut
store that can fulfill the customer order
quickest, thereby meeting its commitment
to deliver hot pizza quickly.
Conceptual Background
Overview of literature on aspects of
customer experience
Theme Study
Customer Experience Berry, Carbone, and
Haeckel (2002); Sousa and
Voss (2006); Gentile, Spiller,
and Noci (2007), Meyer and
Schwager (2007); Naylor et
al. (2008);
Customer Experience
Driver Brand
Chartrand, and Fitzsimons
(2008); Ofir and Simonson
(2007); Keller and Lehmann
(2003); Lee and Rhee
(2008); Gauri, Trivedi, and
Grewal (2008).
Price Ofir et al. (2008); Kopalle
et al. (2009); Bronnenberg
and Wathieu (1996); Wedel
and Zhang (2004); Dorotic,
Verhoef, and Bijmolt (2008);
Gauri, Sudhir and Talukdar
(2008); Noble and Phillips
(2004).
Kamaladevi B.

Theme Study
Promotion Ailawadi et al. (2009); Van
Heerde and Neslin (2008);
Gijsbrechts, Campo, and
Goossens (2003); Chiou-Wei
and Inman (2008); Lwin,
Stanaland, and Miyazaki
(2008).
Supply Chain
Management
Garg et al. (2005); Dant
et al. (2009); Burkle and
Posselt (2009), Xu and Kim
(2008), Neslin et al. (2006);
Patricio, Fisk, and Falcao e
Cunha (2008); Sousa and
Voss (2006); Verhoef, Neslin
and Vroomen (2007).
Location Durvasula, Sharma, and
Andrews (2002); Ghosh and
Craig (2001); Gauri, Trivedi,
and Grewal (2008); Xu and
Kim (2008).
Advertising Chaudhuri & Buck (2005);
Petty & Cacioppo (2003);
Janoschka (2004); Fisher,
Gainer, and Bristor (1997);
Goff et al. (1997).
Packaging & labeling Koirala (2005); Kotler and
Armstrong (2005); Young
(2003); Jugger (1999); Luo
(2005); Wakefield and Baker
(1998); White and Dahl
(2006).
Service Mix Oliver (2001); Parasuraman,
Zeithaml, and Berry (2004);
Baker et al. (2002); Beatty et
al. (1996); Folkes and Patrick
(2003); Meuter et al. (2005);
Van Dolen, Dabholkar, and
de Ruyter (2007); Weijters et
al. (2007).
Atmosphere Baron, Harris and Harris
(2001); Kozinets et al (2002);
Schmitt (1999); Baker et al.
(2002); Kaltcheva and Weitz
(2006); Wakefield and Baker
(1998).
Figure 1. Overview of literature on aspects of
customer experience
Major Factors Influencing Consumer
Buying Decision Process
On the consumer front, many people’s
savings have evaporated in the year 2008,
primarily because of the precipitous decline
in stock prices, suffering real estate markets,
and increasing unemployment. Consumers
thus take greater care in what they buy,
where they buy, and how much they will
pay. Although hardly a sufficient silver
lining, researchers now have the opportunity
to examine more thoroughly many of
the issues discussed in the remainder of
this introduction in a new light. How do
consumers react differently to brand, price,
promotions, supply chain management,
location, advertising, packaging, labeling,
service mix & atmosphere in an economic
crisis? Can retailers take certain actions to
increase patronage, both before and during
a shopping experience? Does consumer
cherry picking change when consumers
face more difficult economic trade-offs?
Will consumers continue to embrace more
expensive and higher quality private-label
merchandise? How should retailers alter
their assortments? Should they continue
to experiment with new categories that
previously appeared only in stores with
differentretailformats?Willpriceelasticities
for substitute and complementary purchases
differ during economic downturns? What
innovative strategies might multi- channel
and online retailers use to gain greater shares
of wallet? And how might retailers adjust
their global sourcing strategies and the way
they work with and develop relationships
with their global vendors? These questions
and many more depend on the major
economic issues that confront consumers
and the retailers they serve.

2009 41
Figure 2. Macro Factors Influencing Consumer
Buying Decision Process
Macro
Factors
Need
Recognition
Information
Search
Evaluation
Purchase
Post
Purchase
Brand x x x x
Price x x x x
Promotion x
Supply Chain
Management
x x x
Location x x x x
Advertising x x x
Packaging &
labeling
x x
Service Mix x x x x
Atmosphere x
The Brand Experience
The customer comes to a retailing
environment with perceptions about two
types of brands: the retail brand (e.g.,
Victoria’s Secret, Starbucks, Wal-Mart,
Macy’s, Best Buy) and the manufacturer or
service brand that is sold in the retail stores
(e.g., Verizon, Ralph Lauren, Tide, Dell,
private label brand). Here, the discussion is
about the retail brand customer experience,
although the ideas put forth below could be
investigated in relation to the manufacturer
or service brand as well.
Background
Customers’ brand perceptions may
influence their customer experience. Recent
researchhasbeguntoinvestigatenewaspects
of this relationship. Specifically, Fitzsimons,
Chartrand, and Fitzsimons (2008) found
that the type of brand and consumers’
perceptions of the brand can influence their
behavior. For example, consumers primed
to think of Apple behave more creatively
than consumers primed to think of IBM. In
addition, Ofir and Simonson (2007) found
thatcustomerexpectations(whenstatedprior
to a service encounter) have a significant
effect on post purchase evaluations of the
shopping experience and the firm. This
suggests that customer brand perceptions (of
the retailer), when primed prior to shopping
experience, might significantly influence the
customer’s experience. It is also important
to consider the reinforcing effects of the
customer’s experience and the brand over
time. Prior research suggests that customer
experience has a significant influence on
the customer’s overall perception of the
brand. In addition, Keller and Lehmann
(2003) propose that the customer mindset
(e.g., awareness, associations, attitude,
attachment and activity) is the key driver of
brand performance (e.g., price premiums,
price elasticities, market share, expansion
success).
Research Discussion
There is much yet to learn about the
influence of brand perceptions on the
customer’s retail experience. There may
be asymmetric effects of brand perceptions
on customer experience. Consumers whose
first impression of a brand is negative can
be influenced by providing them with
non-comparative information, whereas
consumers with positive first impressions of
a brand are influenced more by comparative
information.This suggests an area that is ripe
for future research—namely, understanding
how a customer’s initial perceptions of a
retail brand may influence distinct elements
of the customer’s subsequent experiences
with the brand, and how those experiences
in turn may influence brand perceptions in
the future. In addition, positive customer
brand perceptions may influence customer
experiences differently than negative
customer brand perceptions. As such, future
Kamaladevi B.

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