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Shu-Te University
College of Management
Graduate School of Business and Administration
Master
Determinants of Customer Relationship Management
Performance in Electronic banking sector – The case of banks in
Danang city, Vietnam
Student:
Truong Thi Van Anh
Advisor:
PhD. Fang-Pei Su
Co-advisor: PhD. Le Van Huy
June, 2013
Determinants of Customer Relationship Management
Performance in Electronic banking sector – The case of banks in
Danang city, Vietnam
Student: Truong Thi Van Anh
Advisor: PhD. Fang-Pei Su
Co-advisor: PhD. Le Van Huy
A Thesis
Submitted to
Graduate School of Business and Administration
Shu-Te University
In Partial Fulfillment of the Requirements
for the Degree of
Master of Business and Administration
June, 2013
Department of Graduate School of Business and Management,
Shu-Te University
Determinants of Customer Relationship Management
Performance in Electronic banking sector – The case of banks in
Danang city, Vietnam
Student: Truong Thi Van Anh
Advisor: PhD. Fang-Pei Su
Co-advisor: PhD. Le Van Huy
ABSTRACT
In Vietnam, in spite of Customer Relationship Management (CRM) have been
becoming more popular, especially toward commercial banks, much is still unknown to
service providers how CRM is effective in view point of customers. This study attempts
to address this limitation. The study revolves around determinants of CRM performance
in Vietnam e-banking sector.
To survive in a competitive business environment of financial service firms, banks are
required to become relationship-centric organizations, not only be customer-centric
banks. Therefore variables which have influences on CRM performance should be
identified. This study makes an effort to build a model including four independent
variables (Collaborative CRM technology, Two way communication, Customization,
Employee-client relationship) and one dependent variable (CRM performance). Among
them, the variable ‘Employee-client relationship’ which belongs to culture aspect has
been developed based on Vietnamese customer’s custom in banking area.
The questionnaire was used for data collecting of Danang e-banking customers (356
respondents with responsive rate is 89%). After factor analysis, the results show that
‘Customization’, ‘Employee-Client relationship’, and ‘Two way communication’ have
positive effects on CRM performance.
Although the variable ‘Collaborative CRM Technology’ is expected to affect
positively to CRM performance, the regression analysis does not support this hypothesis.
The outcomes from chapter five would bring up new approaches to banks when
deploying e-banking services. The success does not come from CRM technology but
from how banks support their customers through communication channels and how they
customize interactions with their clients. Furthermore, for the area which face-to-face
encounters rarely occurred, banks still need to pay attention to enhance relationship with
i
customer via visits and individual connection between their employees and customers.
By these ways, suppliers can keep customers better and attain sustainable competitive
advantage.
Keywords: Customer Relationship Management, CRM performance, electronic banking,
collaborative CRM technology, communication, customization, employee-client
relationship
ii
PREFACE
Writing this thesis has been both opportunities and challenges to do. Some
advantages come from my experiences when working at Danang Techcombank. In
addition, customer relationship management is also the subject which I am assigned to
undertake at my university. So this is not only a thesis but also a study makes me very
enthusiastic. I would like to be grateful to those who supported me to complete the
thesis.
At first, I believe that destiny has brought me to my advisor, Ms. Sophia (PhD.
Fang-Pei Su). Under her guidance, I found the way of thinking and working more
professional. I am also grateful for her encouragement which helped me complete this
thesis.
A person who is also important to my process is Assoc. Prof. PhD. Le Van Huy, my
teacher – my co-advisor. He has steered me in the right direction and given me valuable
advices.
I would like to send thanks to my colleague, Master Pham Quang Tin for his advice
on issues relating to data analysis results.
I would like to thank my old chiefs at Techcombank, especially Mr. Dao Le Ngoc
Hai and Mr. Nguyen Phan Truong Giang who gave me suggestions in designing
questionnaire.
I would like to send special thanks to my husband – Mr. Tran Van Vu – who had
four-year experience in banking industry for his useful advices. He also has helped me
in collecting data related to internal bank evaluation.
I would like to thank Dr. Pi-Yun Chen who promoted and assisted me during my
process of caring out my thesis.
At last, I am very grateful to my friends and my family who are my driving forces
and helped me accomplish the thesis easily, especially my little daughter – Tran Hoang
Khue – for coming to my life and gives me more motives.
iii
TABLE OF CONTENTS
ABSTRACT .................................................................................................. i
PREFACE ................................................................................................... iii
TABLE OF CONTENTS ........................................................................... iv
LIST OF TABLES .................................................................................... vii
LIST OF FIGURES ................................................................................. viii
Chapter 1. INTRODUCTION.................................................................... 1
1.1 Research background .................................................................................................1
1.1.1 Commercial banks with e-commerce in Vietnam .............................................1
1.1.2 Electronic banking in Vietnam .........................................................................5
1.1.3 Customer Relationship Management (CRM) in Vietnam commercial banks .10
1.2 Research problem ....................................................................................................13
1.3 Research purpose .....................................................................................................14
1.4 Outline of the thesis .................................................................................................15
Chapter 2. LITERATURE REVIEW ..................................................... 16
2.1 Customer Relationship Management.......................................................................16
2.1.1 Analytical CRM ..............................................................................................17
2.1.2 Operational CRM ...........................................................................................18
2.1.3 Strategic CRM ................................................................................................20
2.1.4 CRM performance ..........................................................................................20
2.2 Two-way communication ........................................................................................21
2.2.1 The concept of two-way communication ........................................................21
2.2.2 Two-way communication in electronic banking sector ..................................21
2.2.3 The influence of two-way communication on CRM performance ..................22
2.3 Customization ..........................................................................................................23
2.3.1 The concept of customization .........................................................................23
2.3.2 Customization in electronic banking sector ...................................................23
iv
2.3.3 The influence of customization on CRM performance ...................................24
2.4 Collaborative CRM technology ...............................................................................24
2.4.1 The concept of collaborative CRM technology ..............................................24
2.4.2 Collaborative CRM technology in electronic banking sector ........................26
2.4.3 The influence of collaborative CRM technology on CRM performance ........27
2.5 Employee-client relationship ..................................................................................29
2.5.1 The concept of Employee-client relationship .................................................29
2.5.2 Employee-client relationship in electronic banking sector ............................30
2.5.3 The influence of Employee-client relationship on CRM performance ...........30
2.6 Relationship-management assessment tool .............................................................30
Chapter 3. RESEARCH METHODOLOGY ........................................ 33
3.1 Research design .......................................................................................................33
3.2 Research purpose ......................................................................................................33
3.2.1 Exploratory research ......................................................................................33
3.2.2 Descriptive research .......................................................................................34
3.2.3 Explanatory research .....................................................................................34
3.3 Research model........................................................................................................35
3.4 Generation of hypotheses ........................................................................................35
3.5 Data collection method ............................................................................................36
3.6 Sampling ..................................................................................................................36
3.7 Measurement instrument .........................................................................................37
3.8 Pilot test ...................................................................................................................39
3.9 Testing goodness of data .........................................................................................40
3.9.1 Reliability........................................................................................................40
3.9.2 Validity............................................................................................................41
3.9.3 Dependence technique ....................................................................................45
Chapter 4. DATA ANALYSIS AND FINDINGS.................................. 49
4.1 Descriptive Statistics ...............................................................................................49
4.1.1
Customers’ bank statistics .............................................................................49
v
4.1.2 Demographic characteristics .........................................................................51
4.1.3 Mean of five research variables .....................................................................51
4.2 Assessing measurement scale ..................................................................................52
4.3 Validity of data ........................................................................................................54
4.3.1 Explanatory factor analysis ............................................................................54
4.3.2 Validity of independent variables ...................................................................54
4.3.2 Confirmatory factor analysis ..........................................................................57
4.4 Multiple regression analysis ....................................................................................60
4.4.1 Regression equation .......................................................................................60
4.4.2 Testing the conformity of research model ......................................................60
4.4.3 Testing hypotheses of relationship between the independent variables and the
dependent variable ..........................................................................................................60
4.5 Result of internal evaluation of two banks ..............................................................64
4.5.1 Evaluating four elements by employees of two banks ....................................64
4.5.2 Comparison with evaluating result by customers...........................................65
Chapter 5 . CONCLUSIONS AND IMPLICATIONS .......................... 67
5.1 Summary of study....................................................................................................67
5.2 Implication ...............................................................................................................68
5.2.1 Theoretical Implications .................................................................................68
5.2.2 Managerial Implications ................................................................................70
5.3 Research limitations and suggestion for future research .........................................74
5.4 Final conclusions .....................................................................................................74
REFERENCES .......................................................................................... 75
APPENDIX ................................................................................................ 80
APPENDIX A. QUESTIONNAIRES .............................................................................80
APPENDIX B. DATA ANALYSIS RESULT ................................................................90
AUTOBIOGRAPHY ............................................................................... 102
vi
LIST OF TABLES
Table 1. Banks implementing Internet banking.................................................................7
Table 2. Banks implementing SMS banking .....................................................................8
Table 3. Measurement of components .............................................................................37
Table 4. Measurement of components after pilot test .....................................................38
Table 5. Purpose and Time of usage................................................................................50
Table 6. Respondents’ demographic characteristics........................................................51
Table 7. Descriptive statistics for independent and dependent variables ........................52
Table 8. Reliability statistics for all items .......................................................................53
Table 9. Rotated Component Matrix of Independent variables .......................................55
Table 10. Component Matrix of dependent variable .......................................................57
Table 11. Result of confirmatory factor analysis ............................................................57
Table 12. Model Summary ..............................................................................................60
Table 13. Coefficients......................................................................................................61
Table 14. Internal evaluation of Vietcombank and BIDV...............................................64
Table 15. External evaluation of Vietcombank and BIDV .............................................65
Table 16. Remaining loading items .................................................................................67
Table 17. Types of consumers .........................................................................................72
Table 18. Use of media by various customer types .........................................................73
vii
LIST OF FIGURES
Figure 1. Non-cash payment rates from 2001-2010 (%) ...................................................2
Figure 2. Characteristics of a bank which applied SSL .....................................................3
Figure 3. Rates of banks which deployed internet banking and SMS banking .................9
Figure 4. Number of ATM and POS ...............................................................................10
Figure 5. Card market share of banks in the early of 2010 .............................................10
Figure 6. Top 10 countries with highest asset growth of banking sector ........................12
Figure 7. The contact cycle..............................................................................................19
Figure 8. Customer retention program ............................................................................24
Figure 9. Research model of Performance in CRM ........................................................28
Figure 10. CRM value generation process ......................................................................28
Figure 11. Determinants of CRM performance in e-banking individual customer’s view
.........................................................................................................................................35
Figure 12. Selecting a Multivariate Technique ...............................................................46
Figure 13. The null model ...............................................................................................58
Figure 14. The first-order factor model ...........................................................................59
Figure 15. Empirical model .............................................................................................62
Figure 16. Internal evaluation by employees of Vietcombank and BIDV ......................65
Figure 17. External evaluation by customers of Vietcombank and BIDV ......................66
viii
Chapter 1. INTRODUCTION
1.1 Research background
1.1.1 Commercial banks with e-commerce in Vietnam
According to Vietnam e-commerce report from Ministry of Industry and Trade, by
the end of year 2005, the first stage for e-commerce development in Vietnam had been
completed, with e-commerce being established and officially recognized by law. Two
years after the promulgation of the e-Transaction Law, e-commerce has firmly set foot
in Vietnam and maintained its strong momentum of growth, while continuing to expand
its impacts across various socio-economic activities of the nation. With thorough
preparation and vigorous efforts demonstrated by both the business community and the
public sector, from year 2006 e-commerce in Vietnam progressed to the second stage of
rapid growth.
One of the quantitative measures of e-commerce investment is the rate of businesses
having websites, which steadily increased over the years and reached 38% in 2007,
meaning 4 out of 10 surveyed enterprises have established their own websites. Also, the
2007 survey showed that 10% of businesses participated in e-marketplaces, 82% have
local area networks (LAN), and most notably, 97% have access to Internet with ADSL
being the major mode of connection.
At the macro policy level, at the beginning of year 2007 a significant text related to
e-payment came into effect, namely Prime Minister Decision 291/2006/QĐ-TTg dated
29 December 2006 approving the 2006-2010 Plan for Non-cash Payment
Implementation and Vision towards year 2020. In the very first year of this Plan
implementation, the banking sector recorded several outstanding accomplishments.
Firstly, the whole banking sector has had 15 banks installing and utilizing 4,300 ATMs
and 24,000 POS devices. Secondly, 29 banks have issued nearly 8.4 million payment
cards and formed several card alliances, of which the two alliances Smartlink and
Banknetvn account for a combined 90% market share and are working together to unify
the national market for card payment. Commercial banks have set roadmaps for gradual
1
transition from magnet cards to cards using electronic chip. Thirdly, information
technology has been applied to most of transactions among and within the State Bank,
commercial banks, and credit institutions.
Until the early of year 2008, around 20 banks are providing customers with Internet
Banking and SMS (short message service) banking services. Card payment has become
more popular, with increasingly diverse application. Suppliers of electronic payment
services expanded to include other types of businesses aside from banking institutions.
Some payment gateways models have been formed and started to function. 2007 is also
a milestone year in the sense that online payment was first implemented on some
Vietnam e-commerce websites, namely Pacific Airlines, 123mua!, Viettravel and
Chodientu. Non-cash payment is one of the most important duties to develop Vietnam
economy that government specially concentrated. Figure 1 shows non-cash payment
rates in 2001-2010 stage:
Figure 1. Non-cash payment rates from 2001-2010 (%)
Source: Vietnam e-commerce report 2011
Various legal texts in specialized application areas have been issued, contributed to
fulfilling the legal system for Vietnam e-commerce. In the banking sector there are State
Bank Governor’s Decisions on Procedures for Designating, Managing and Using
Digital Signatures and C/A Services in the Banking Sector, as well as Procedures for
Bank Cards Issuance, Payment, Usage, and Support Services. The Prime Minister has
also promulgated Decision 20/2007/QD-TTg stipulating Payment of State Employees’
Salary via Bank Accounts.
2
According a report of The state bank, until the end of 12/2011, there were 50 banks
on Vietnam market (included state-owned commercial banks, policy banks, joint-stock
commercial banks, joint-venture banks and banks with 100% foreign capital). Within 45
banks which deployed online transactions as different levels, there were 36 banks had
applied SSL (almost of VeriSign provider). A bank has used SSL can be easily
recognized as shown in Figure 2.
Address bar
turns into
green
‘http’ turns
into ‘https’
(connected
to server
which used
SSL)
Figure 2. Characteristics of a bank which applied SSL
Decree 35/2007/NĐ-CP dated 8 March 2007 on electronic transactions in banking
activities was the third decree promulgated in 2007 as a guiding text for the Etransaction Law. This Decree mainly focuses on guiding the application of E-transaction
Law in specific banking operations, as well as providing necessary legal conditions for
the development of secure and effective electronic transactions in the banking system.
The Decree is composed of 5 chapters, with 29 articles governing several major issues
as followed:
3
-
Banking electronic transactions (chapter 2): identifies the scope of banking
electronic transactions; stipulates conditions for electronic transactions;
stipulates types of electronic signatures used in banking system and electronic
signature certification providers.
-
Electronic communications in banking operations (chapter 3): provides
supplementing guidance on regulations on content, legal validity and form of evouchers; principles of making, controlling, processing, conversion, archive and
storage of e-vouchers in banking electronic transactions; signing and legal
validity of electronic signatures on e-vouchers.
Banking was one of the very first sectors that embraced ICT applications in Vietnam.
Electronic transactions were implemented in banking operations since the end of 90s.
Prime Minister’s Decision No. 196/TTg dated 1 April 1997 and Decision No.
44/2002/TTg dated 21 March 2002 enabling the use of electronic communications and
electronic signatures in bank accounting and inter-bank payment transactions were
regarded the first legal texts relating to electronic transactions in Vietnam. However,
only with the promulgation of the Decree on Electronic Transactions in Banking
Activities did the legal framework for electronic banking come to shape, enabling
broader implementation of electronic transactions in banks’ operations, promoting the
development of electronic banking services, and setting the foundation for payment
solutions for e-commerce in Vietnam.
In spite of this, security and privacy policy is one of the biggest problems when
implementing e-commerce. A survey conducted by the end of year 2006 showed that
only 26% websites announced data privacy policy. The rest ones had lack specific
commitments to customers on due practice of data collection and usage. B2B portals –
the most professional e-commerce websites – boast the highest rate of websites having
data privacy policy (57%), while B2C and C2C websites, though dominate in number
and target individual consumers, have a much lower rate of privacy policy disclosure.
Almost Vietnam commercial banks oriented themselves as retail banks and conduct
marketing activities through various modern channels. Along with this issue is legal
4
framework for commercial emails and anti-spam measures and how commercial banks
can ensure that their marketing messages receive permission from their customers.
In Vietnam, advertising through electronic means such as email, SMS, e-bulletins,
etc, is becoming more and more popular. The advantages of these advertisement
channels are high speed, low expenses, good interactive capability, and wide
dissemination multitude. However, without proper monitoring mechanism, advertising
through emails and SMS may cause adverse effects such as violating consumers’
privacy and hammering the operation of the whole information system. These issues call
for the establishment of a legal framework to regulate electronic advertisement practices,
so as to protect consumer’s interests and at the same time enable businesses to take full
benefits of those advertisement channels. The Law on Information and Communication
Technology (which was approved by the National Assembly on 29 June 2006) defines
“Spams are emails and messages sent to recipients who do not wish and/or have the
obligation stipulated by Law to receive”.
1.1.2 Electronic banking in Vietnam
The new developed step of Vietnam economics was marked in 2007. This is the first
year witnessed Vietnam’s accession to World Trade Organization (WTO) officially. As
well as the other fields, Vietnam banking industry is facing to extreme challenge – the
violent competition of strong banks, financial corporations all over the world. These
corporations have more than half century experience in deploying diversified financial
services to serve businesses and individual customers based on applying information
technology (IT) systems professionally and. One of strategies that have been developed
by Vietnam banks since 2007 is investing in IT infrastructure building for basic banking
operations and developing derivative banking proficient through payment card systems,
online accounts, online transactions, auto teller machine – ATM, point of sales – POS
systems.
General developing trend of banking system is reducing offer services through
traditional channels and increasing offer services through modern channels to replace
traditional channels. In spite of this, depend on specific condition, development of
supply system is different from different countries. Electronic banking as the result of
5
this has become one of efficiency means helps banks easier approach customers,
increase serving time. E-banking need to be cared as intermediate bridge for business
activities, helps establishing new infrastructure, enhancing operation quality of
customer-centric banks.
Electronic banking is considered as any banking activity implemented by electronic
means such as ATM, customer support center, personal computer, telephone… These
means can be used to pay bills, transfer accounts, buy shares and supply other financial
services. Both commercial banks and non-banking financial organizations can offer ebanking services such as pay bills, short-term transaction accounts… (Vanessa, 1998).
Online banking is the fastest growing service that banks can offer in order to gain
and retain new customers (Moody, 2002). The role of electronic banking is increasing in
many countries. There are several reasons for that. First of all, this is due to increasing
role of electronic money as a main instrument of electronic banking. Secondly,
transition to electronic money is only possible through a wide implementation of
electronic banking in the sense that issuing institutions have been developing
simultaneously with institutions accepting e-money.
The mainstream economic theory treats electronic banking as a financial innovation
that is effectuated by a vision of bank as a profit-generating facility. It follows then, that
‘a change in the financial environment will stimulate a search by financial institutions
for innovations that are likely to be profitable’ (Mishkin, 2000).
Foreign banks are developing electronic banking services strongly. In Vietnam, this
is new area of operations, only some department in banks develops separately such as
home banking, mobile banking… or several services such as establishing and
developing website of banks (Hữu, 2005). Nowadays, electronic banking exists in two
types: (1) online banking only exists in internet environment, offer 100% services
through online environment; and (2) combination between traditional commercial
banking systems and electronicize traditional services, offer old products/services on
new distribution channels. At Vietnam, electronic banking mainly develops based on
the combination. To give a concrete, there are several commercial banks offer home
banking service (Vietcombank, Vietinbank, ACB, Eximbank... and two foreign banks,
6
ANZ and Citibank), phone banking service (Vietcombank, ACB, Techcombank, HSBC,
ANZ, and Citibank…), and mobile banking service offered by Incombank (now become
Vietinbank), ACB, and Techcombank… In addition, other banks only limited at
establishing websites to introduce them and supply service information (Ngan and Hai,
2006). To date, more than 80% transactions between Vietnam banks are deal with
through computers at different levels, 85% transactions between banks and their
customers deal with by computers or modern means. Government aimed to 95 million
cards at 2020, cash payment also not much more 18% and 80% transactions between
businesses through banks at 2020 (Thoa, 2007).
Banking industry is considered a fast growing industry with the dynamic
participation from domestic and international enterprises. With the development of
technology, banks should not ignore this advantage in improving their services. This is
proved by the increase in the number of banks providing Internet banking recently.
Internet banking service is a newly common concept recently when more and more
banks provide that service since 2004 (see further more in Table 1).
Table 1. Banks implementing Internet banking
No
Banks
Information features
Payment features
Account
Balance
Banks’ Transfer Paying Others
info
info
info
bills






1
Vietcombank




2
VIB


3
Habubank




4
Incombank


5
Phuong Nam bank



6
Marine bank



7
Military bank






8
Techcombank



9
Saigon-Hanoi bank



10
Saigon bank






11
East Asia bank


12
Saigon Incombank





13
Citi bank


14
ANZ





15
Indovina




16
Eximbank






17
ACB



18
An Binh bank
*Other services consist of managing securities transactions, opening and ending L.C,international
money transferring, paying debts, registering bank services, paying through websites
7
Source: Survey by E-Commerce Department, Ministry of Industry and Trade, December 2007
Together with Internet banking, short message service (SMS) banking was born as
the following step in the applying information appliance to improve banking services.
This is a type of mobile banking, a technology-enabled service offering from banks to
their customers, permitting them to operate selected banking services over their mobile
phone which uses short messaging.
Taking the advantage of the popularity of SMS, many banks have added a new
utility to serve customers better. SMS banking services can be categorized into 2 utility
groups based on needs, which are information and payment utilities. This categorizing is
for evaluating the scope of supplied service and reflects the present demand of the
market. Paying utility is the main difference within banks. Among 16 banks having
SMS banking, only 6 banks have paying utility. This group is divided into 2 categories,
the first one is payment for banks’ activities like credit card, money transferring and
paying bills or paying online. The second is presented in Table 2.
Table 2. Banks implementing SMS banking
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Banks
Providing information utility
(1)
(2)
(3)



(4)
Paying utility
(5)
(6)
Vietcombank

VIB



Habubank




Incombank



Phuong Nam bank




Marine bank




Military bank

Techcombank



Saigon-Hanoi bank



Saigon bank





East Asia bank





Saigon Incombank




Eximbank



ACB



Viet A bank


VID Public bank
(1) Account balance; (2) Account statement; (3) Interest rate, Foreign exchange rate, ATM
locations and branches; (4) Credit limit, credit’s activities, information about LC and export
import documents;
(5) Money transfer; (6) Paying with credit card, paying bills, buying online
Source: Survey by E-Commerce Department, Ministry of Industry and Trade, December 2007
8
Majority of banks has both services SMS and Internet banking. Both provide utilities
to customers and support each other. Of all banks having both services, the majority of
domestic commercial banks (see Figure 3). This is a positive sign about the dynamic
manner of Vietnamese banks. International banks in Vietnam such as ANZ and Citibank
have only Internet banking.
Not exist
Exist
Figure 3. Rates of banks which deployed internet banking and SMS banking
Source: Vietnam e-commerce report (Linh et al., 2011)
According to Vietnam e-commerce report (2009), card payment services also saw
rapid growth. By the end of 2009, 45 banking institutions have issued more than 21
million payment cards with card revenue in domestic currency totaling VND 25,000
billion and in foreign currency registering USD 2 billion. The whole banking system has
more than 9,500 ATM and 33,000 POS devices and in the next 2 years, this number has
been doubled (Figure 4). Currently the State Bank of Vietnam is perfecting the legal
foundation for promoting the development of new payment tools and services. The
systems of the two biggest card payment alliances, namely Banknetvn and Smartlink
with more than 90% of the national card market, have been interconnected. In the
upcoming time, the system of VNBC and ANZ will be connected with BanknetvnSmartlink to form a single, integrated card payment system. Most of electronic payment
services in Vietnam were developed on the platform of core banking technology
transferred from overseas.
9
Figure 4. Number of ATM and POS
Source: Vietnam e-commerce report (Linh et al., 2011)
The number of ATMs rocketed from 1,800 in 2005 to 11,700 in 2010 while the
number of credit and debit cards issued during 2008 – 2010 doubled to 31.7 million.
This was the result of the increase in households’ income and demands for retail
banking (Linh et al., 2011).
Another characteristic of card payment development is concentrating trend, 5 banks
hold 80% market share of national card payment (among 49 total banks) (see Figure 5.)
Others
Figure 5. Card market share of banks in the early of 2010
Source: Vietnam bank card association (2010)
1.1.3 Customer Relationship Management (CRM) in Vietnam commercial banks
Customer relationship management (CRM) is now adopted as a necessity and its
methods and experiences are applied in many industries because of its great role in
10
becoming more customer focused to deal with the competitions between companies and
to retain their current and loyal customers to gain more profit and reduce the costs
(Newell, 2000).
Banks deal with individual consumers or customers so they want CRM for its
analytical capability to manage customer defection (churn) rates and to enhance crosssell performance. Data-mining techniques can be used to identify which customers are
likely to defect, what can be done to win them back, which customers are hot prospects
for cross-sell offers, and how best to communicate those offers. Banks and
telecommunication companies want to win a greater share of customer spend (share of
wallet). In terms of operational CRM, they are both transferring service into contact
centers in an effort to reduce costs (Buttle, 2004). Nowadays, commercial banks are
trying to build better relationships to existing as well as new customers to increase
customer loyalty and long-term retention. Therefore, many of Vietnam banks are trying
hard to be more efficient in using technological resources and strategies of customer
relationship management (CRM).
The size of Vietnamese banking sector has increased significantly in recent years.
According to IMF’s data, the total assets of banking sector had doubled from VND
1,097 trillion (USD52.4 billion) in 2007 to VND 2,690 trillion (USD 128.7 billion) in
2010 (Figure 6.). This figure was forecast to reach VND 3,667 trillion (USD 175.4
billion) by the end of 2012. Vietnam was short-listed in the top ten countries having the
highest asset growth of the banking sector by The Banker, ranking second after China.
Eximbank was the only Vietnamese bank in the top 25 banks achieving high asset
growth in 2010, ranked 13th.
11
Figure 6. Top 10 countries with highest asset growth of banking sector
Source: www.thebankerdatabase.com
Vietnam’s retail banking development is still in its infancy. After 2010, retailing is
expected to become one of the major operations of the banking services market. Despite
strong development, the retail banking services of Vietnamese commercial banks
remains weak because of the low competitiveness of service products. Foreign and
Vietnamese owned banks have developed different modernization strategies. Foreign
banks namely ANZ, HSBC, Standard Chartered, UOB, SMBC, Deutsche Bank occupy
the market through becoming strategic shareholders of some local joint stock banks
(FineIntel, 2010).
In the past, the scope of foreign banks has been limited due to a geographical
restriction of a single branch per city. Greater flexibility for foreign banks has arrived
because of Vietnam’s accession to the World Trade Organization (WTO). One of the
commitments of Vietnam upon joining the WTO is that from January 1, 2011, foreign
banks will be allowed to operate on par with domestic banks. This means foreign banks
will have the same full rights to providing banking services, which domestic banks
currently have. In year 2011, domestic banks have around 90 percent share of the retail
market in Vietnam. However, foreign banks are fast becoming strong competitors in the
banking retail market by providing services with high technologies, which domestic
banks do not have. In addition, according to Nielsen’s survey, the average Vietnamese
12
person uses an ATM machine 0.5 times per week. Changing consumer behavior to suit a
credit-based rather than a cash-based economy seems a long way off (FineIntel, 2010).
CRM is one of the most growing trends in banking industry these days, especially in
electronic environment and high investments have been spent on its technologies in
order to keep the customers satisfied. Also, it is considered as the top banks'
implementation programs priority which is today more seen in e-banking. In today's
competitive markets, an aggressive competition between banks is seen more than in the
past. They have realized that relationship with customers and its management are
significant factors to win this race especially in e-banking in which face to face
interactions does not exist. The applications of customer-centric strategies and programs
of customer relationship management (CRM) help banks to build long term
relationships with customers and result in increasing their income. Therefore, in the
banking sector, CRM is of strategic significance because of the effects it has on
customer satisfaction and retention which is the final goal in any successful businesses
(Blery and Michalakopoulos, 2006).
Some domestic banks in Vietnam have been applying Customer Relationship
Management to maintain and develop their customers. Especially in electronic banking
sector, local banks are facing to extremely potential competitive threats from foreign
banks. If their CRM implementation brings effectiveness to them, local banks can save
cost as compared with traditional channels, making customers become more valuable
and building up strong switch barriers.
1.2 Research problem
There are many researches about CRM in view point of managers, suppliers all over
the world but there is lack of papers about CRM in customers’ view. Because of mutual
property of relationship, researching in CRM performance need consider customers
perception and how they perceive about relationship between their firms and them.
Although CRM has become the in-thing of marketing strategies nowadays, it is
unfortunate that many people are still confused about the actual domain of CRM which
perceives customer and service providers the act as major players. It is very important to
13
measure the performance of CRM in any organization (Wahab et al, 2009). While
numerous studies relating to CRM framework, IT, implementation strategic, and cases
(e.g. Zablah et al., 2004; Lindgreen et al., 2005…) have been conducted, there has been
lack of academic effort addressing the issue of CRM performance and no papers
conducted about CRM performance in customer view.
In Vietnam, recent studies in banking services concentrated in effects of CRM or
how to enhance performance of customer relationship management in banks. But which
factors have influences on CRM success is not conducted in Vietnam. So this study will
consider the determinants of customer relationship performance of electronic banking
services in Vietnam – one of the most important components makes competitive
advantage of banks.
From research problem mentioned above, in view point of electronic banking
customers, following research questions are identified:
-
What factors (two way communication, customization, collaborative CRM
technology, employee-client relationship) influence CRM performance of
Vietnam commercial banks?
-
How do these factors have influences on CRM performance of Vietnam
commercial banks?
-
How can domestic commercial banks adapt certain variables in order to enhance
their performance of CRM?
1.3 Research purpose
Nowadays, customers have access to a variety of services and products and when
they do not meet their needs easily, they can choose those institutions that provide them
with fast and high quality products or services. Therefore, companies try to use unique
strategies to retain their current customers instead of customer acquisition which needs
more investments. To reach this purpose, new and different tools and mindset are
required (Winer, 2001). Also considering human and organizational resources as much
as technological capabilities is necessary to manage good relationships with the
customers (Keramati et al., 2008).
14
Barnes (2001) stated that firms were likely to fall into the trap of believing that an
enterprise could simply decide to have a relationship with a customer, “whether or not
that customer wants one”. The result is the mistaken belief that customer relationships
can be built or even imposed through the creation of customer databases or frequent
shopper programs.
Thus, the goal of this paper: (1) to define independent variables affect to
performance of customer relationship management in electronic banking environment,
(2) to determine how strong these factors linked with CRM performance, and (3) to
make implications for management to enhance CRM performance of e-banking services.
1.4 Outline of the thesis
This thesis is divided into five chapters. The first chapter which is the introduction
presents the background of the research, the research questions, and research objectives.
Chapter two provides an overview of literature review on the main previously related to
CRM and factors have effect on CRM. Chapter 3 deals with the frame of reference,
presents the adopted models for the study, and formulates the research hypothesis and
the research method.
Chapter fourth analyzes the empirical data after conducting survey by using
questionnaire. This research ends with chapter five which gives managerial and
theoretical implications and further research in future.
15
Chapter 2. LITERATURE REVIEW
2.1 Customer Relationship Management
In spite of the progress that is being made, there is still some confusion about the
meaning and implications of CRM. One of definitions originates from Metagroup, in
2000, defined CRM as ‘the automation of horizontally integrated business processes
involving front office customer contact points (marketing, sales, service and support)
via multiple, interconnected delivery channels’. In this description, CRM is positioned
in the ‘IT comer’. Technology facilitates or makes customer contact possible between
employees from different departments via the Internet, telephone, and the ‘face-to-face’
channel. On the internet, technology essentially replaces people, and a human-machine
interaction arises. In telephone and face-to-face contract, IT plays a more supportive
role and makes sure that the supplier’s employees are in a better position to help the
customer.
An entirely different definition suggests that CRM is ‘a process that addresses all
aspects of identifying customers, creating customer knowledge, building customer
relationships, and shaping their perceptions of the organization and its products’. The
role of technology is not even mentioned in this definition. CRM is still referred to as a
process, or rather a sequence of activities; however, this definition does not specifically
state that IT is necessary to perform these activities. At the same time, this definition
requires that more attention be paid to the customer and the goal on hopes to achieve
face-to-face customer.
The Gartner group’s definition from 2004 goes a step further. This research agency
describes CRM as ‘an IT enabled business strategy, the outcomes of which optimize
profitability, revenue and customer satisfaction by organizing around customer
segments, fostering customer-satisfying behaviors and implementing customer-centric
processes’. With definition of Gartner group – CRM as an IT enabled business strategy,
we can determine influential factors in CRM performance of electronic banking sector.
16
CRM means different things to different people, a business practice focused on
customers. In general, CRM can be thought about at three levels: strategic, operational
and analytical. CRM is the core business strategy that integrates internal processes and
functions, and external networks, to create and deliver value to targeted customers at a
profit. It is grounded on high-quality customer data and enabled by IT (Buttle, 2004).
In general, CRM is both an operational and an analytical process. Operational CRM
focuses on the software installations and the changes in process affecting the day-to-day
operations of a firm. Analytical CRM focuses on the strategic planning needed to build
customer value, as well as the cultural, measurement, and organizational changes
required to implement that strategy successfully (Ruyter et al., 2001). In CRM
conception, analytical CRM is what a firm has to know about customer to make him
more valuable, operational CRM is what a firm has to do to make customers more
valuable. So in customer view, operational CRM has more meaningful to them their
perception about operational CRM may have influence on CRM performance.
2.1.1 Analytical CRM
Analytical CRM is included customer identification (define, collect, link, integrate,
recognize, store, update, analyze, make available, secure) and customer differentiation
(prioritize by value, understand different needs) (Peppers and Rogers, 2004).
Buttle (2004) supposed that analytical CRM is concerned with exploiting customer
data to enhance both customer and company value. Analytical CRM is built on the
foundation of customer information. Customer data may be found in enterprise-wide
repositories: sales data (purchase history), financial data (payment history, credit score),
marketing data (campaign response, loyalty scheme data), service data. Beside these
internal data, customer data can be collected from external sources: demographic and
lifestyle data from business intelligence organizations, for example. With the
application of data mining tools, the company can then interrogate these data. Intelligent
interrogation provides answers for questions such as: Who are the most valuable
customers? Which customers have the highest propensity to switch to competitors?
Which customers would be most likely to respond to a particular offer?
17
Analytical CRM has become an essential part of effective CRM implementation.
From the customer’s point of view, analytical CRM can deliver better, timelier, even
personally customized, solutions to the customer’s problems, thereby enhancing
customer satisfaction. From the company’s point of view, analytical CRM offers the
prospect of more powerful cross selling and up-selling programs and more effective
customer retention and customer acquisition programs (Buttle, 2004).
2.1.2 Operational CRM
Operational CRM is focused on the automation of the customer-facing parts of
businesses. Various CRM software applications enable the marketing, selling and
service functions to be automated (Buttle, 2004).
According to Peelen (2005), the topic of operational CRM is the contact cycle. The
contact cycle is initiated with the target group, new customers are welcomed in, initial
contacts are followed up and the dialogue between customer and supplier is expanded
upon. Communication takes place via different channels, and at different times and
locations. The relationship strategy which has been designed is implemented through a
learning dialogue in which both parties learn from one another continuously and make
use of the knowledge gained. Operational CRM forms a substantial part of the CRM
system. Figure 7 illustrates the CMAT model which is used to describe it. The centre of
the figure presents the contact cycle in the block ‘Customer Management Activity’. The
contacts form a process in themselves, the communication process, and will have to fit
in closely with other processes in the organization so that goods or services purchased
may be managed, produced, purchased, supplied and serviced. They are dependent on
the underlying (IT) infrastructure (the figure’s bottom) for their success.
During the contact process between customer and supplier, the right proposition will
have to be made to the right customers (the left side of figure). By conducting a
dialogue, companies will have to determine what customers would like in a certain form
at a certain time so that the right experience may be created and a competitive advantage
may be realized (top of figure). Continuous measurement of the contacts is desirable in
order to learn from the effectiveness and efficiency of one’s own behavior and the
customer preferences (right side of figure).
18
Competitors
The customer experience
Customer management activity
Analysis
and
Planning
Targeting
The
Propositon
Enquiry
management
Win back
Managing
problems
Measuring
the Effect
Welcoming
Customer
development
Getting to know
customer
Processes
People and Organization
Technology
Figure 7. The contact cycle
Source: Qci Assessment Ltd (2004)
Peelen (2005) also stated that people tend to grab the telephone more quickly than
they used to and use it for asking direct questions to which they expect a prompt answer
from suppliers. They expect a great deal from suppliers. In many cases, the level of
accessibility and the supplier’s availability by telephone should be high. Firms should
expect and be willing to communicate with customers for many hours per day, be
capable of gaining quick insight into the customer situation and be able to provide
customers with solutions – in an efficient, and also in an empathetic and communicative
manner. This all arranged in the call or contact center. As was indicated, telephone
19
contact is not the only channel managed from the call center. Faxed and e-mailed
correspondence may also be handled by the agents in the center.
2.1.3 Strategic CRM
Strategic CRM is focused on the development of a customer-centric business culture.
This culture is dedicated to winning and keeping customers by creating and delivering
value better than competitors. The culture is reflected in leadership behaviors, the
design of formal systems of the company, and the myths and stories that are created
within the firm.
In a customer-centric culture, resources would be expected to be allocated where
they would best enhance customer value, reward systems to promote employee
behaviors that enhance customer satisfaction, and customer information to be collected,
shared and applied across the business. “Heroes” of business need to be found to deliver
outstanding value or service to customers (Buttle, 2004).
2.1.4 CRM performance
Performance is defined as the potential for future success of actions in order to reach
the objectives and targets (Lebas, 1995).
CRM performance evaluation metrics are related to customer relationship strength,
sales effectiveness, and marketing efficiency (Kim et al., 2004). According to Rootman
(2006), customer relationship refers to the degree and manner of purposeful
relationships formed between banks and their clients. Rootman concluded that two way
communication, employee attitude, employee knowledge, efficiency of banking services
had positive effects on customer relationship management. In terms of customer view,
CRM performance is perceived to relate to customer relationship strength. This paper
also concentrates on evaluation of CRM performance.
Wu et al. (2009) affirmed CRM performance was affected by customer profile and
customer participation. Keramati et al. (2010) supposed that CRM processes were
affected by technological CRM resources and infrastructural CRM resources, had
influence on CRM process capabilities, and enhanced organizational performance.
Greve and Albers (2006) also assessed CRM technology as one of influential factors in
CRM performance. They were also based on customer life cycle to show level of
20
relationship between firms and customers (initiation performance, maintained
performance, retention performance).
2.2 Two-way communication
Relationships cannot be imposed upon customers. Businesses need relationship
recognition from their customers (Pepper and Rogers, 2004). In customer view about
relationship, two-way communication may be one of explicit ways that imply
recognition.
2.2.1 The concept of two-way communication
Communication is the delivering of a message or information, through various
means, from one individual or group to another (Dodd, 2004; Joiner, 1994). Through
communication, information is transferred, and the use of different methods or media
creates an understanding between two or more parties. A firm would not be able to
function without communication, as its management would not be able to convey
important information to employees (downward communication) and vice versa
(upward communication) or to customers/clients (two-way communication). Two-way
communication is the degree of appropriate and sufficient communication from bank
management to clients, and vice versa (Rootman, 2006).
2.2.2 Two-way communication in electronic banking sector
A service provider needs to evaluate the interaction situation with clients and
establish what will motivate them. The service provider should use communication
techniques that will motivate a client to make use of the service. Firms communicate
with clients through specific communication tools, namely mail, e-mail, websites,
telephone, fax, chat-rooms, contact centers, help-desks and complaints lines. Firm-client
communication interaction can occur through conventional or new direct-to-customer
(DTC) methods. The conventional elements include advertising, sales promotion, public
relations, and personal selling (Marx et al. 1998).
In the electronic banking services, banks can communicate with clients through a
variety of media. Messages can be communicated through individuals (personal
communication) and/or the mass media (impersonal communication). Clients can
21
receive information through the mail, which can constitute letters, statements or
brochures, e-mail, telephone, SMS, radio, television, the press.
2.2.3 The influence of two-way communication on CRM performance
Communication is a human activity that connects people and creates relationships
between them. Firms can make use of effective communication to shape client
relationships (Swartz and Iacobucci, 2000). In a firm-client relationship, communication
has to become a two-way process or dialogue (Christopher et al. 2002).
Two-way communication refers to the communication between a firm and its clients.
Effective and efficient communication with external markets is a fundamental
marketing responsibility of any firm. Business communication between a service
provider and clients is often crucial in the service delivery process (Marx and Van der
Walt, 1993). Little and Marandi (2003) also states that partnerships or relationships,
between firms and their consumers, are built on and maintained by communication. This
is emphasized by Mudie and Cottam (1999) that state that communication can add value
to the service in the eyes of the consumer. Communication is a continual series of
dialogue or “conversations” with clients, with the goal being to get them to view the
service firm as a partner (Swartz and Iacobucci, 2000). Communication with clients
should be viewed as a two-way mechanism rather than as a one-way “promotion”.
Clients want to be heard and really listened to, rather than being promoted to
(Wilmshurst and Mackay, 2002).
Online interaction is an attractive medium for firms to gather preference information
and to engage in two-way communication with customers so as to adapt supply to
heterogeneity in demand. The communication methods used by both service firms and
clients do not always lead to two-way communication. Two-way communication does
not always lead to in-depth listening by and learning in the other party. However, the
value of two-way communication is evident because of its potential for spontaneity and
creativity. Firms create value for clients through communication methods that ensure
two-way communication (Buttle, 2004).
Two-way communication occurs when firms listen to their consumers, and with
better interaction between consumers and firms. Communication before, during and
22
after transactions can build and maintain relationships. Effective communication is of
the utmost importance in order to ensure a successful firm and relationships with clients
(Mudie and Cottam, 1999; Duncan and Moriarty, 1998). Service providers need to listen
to and interact with clients, and accordingly design and provide a service that will lead
to continued value exchanges between the two parties (Christopher et al. 2002). If a firm
listens to its target market through effective communication, it will know exactly what
its clients want, how they want it, when they want it and what they are willing to pay for
the service (Anderson and Zemke, 1991). The service firm will be able to deliver its
promises, as expected, to its clients.
Tschohl (1991) indicates that a firm’s communication with its clients is important, as
such communication will inform the firm whether clients are satisfied, which services
clients use, what clients expect and are willing to pay, and what clients’ preferences are.
Therefore, sufficient two-way communication is important for service firms, including
banks, and may influence the degree of CRM performance of such firms.
2.3 Customization
2.3.1 The concept of customization
The enterprise should adapt some aspect of its behavior toward a customer, based on
that individual’s needs and value. To engage a customer in an ongoing Learning
Relationship, an enterprise needs to adapt its behavior to satisfy the customer’s
expressed needs. This might entail mass-customizing a product or tailoring some aspect
of its service (Peppers et al., 1999)
2.3.2 Customization in electronic banking sector
Customization goes beyond communicating with customers and is also about the
creation of products for individuals (Rootman, 2006). Academicians call such
customization, "versioning" which is easy and cheap to do. The point that should be
noticed is that versioning is easier to do for services and intangible products than for
physical products; however companies can use the additional information gained from
customers to tailor at least the appearance of products (Winer, 2001; Farquhar, 2004;
Smith, 2006).
23
2.3.3 The influence of customization on CRM performance
Winer (2001) states that customization is about the creation of products and services
based on each customer's taste. In this way, each customer is able to choose a product
from a list or order the item with the favorite quality or characteristics.
Figure 8. Customer retention program
Source: Winer (2001)
Winer stated that customization had a positive effect on customer relationship
management satisfaction (Figure 8). Customization also belongs to operational CRM
(Peppers and Rogers, 2004). So, customization has influence on CRM performance in
customers’ view. This is supported by the ICSB when they establish CRM quick scan
plot. In this scan, they also consider customization as an operational management issue.
2.4 Collaborative CRM technology
2.4.1 The concept of collaborative CRM technology
CRM technology is the information technology that is deployed for better
management of customer relationships (Reinartz et al., 2004). It includes front office
applications that may support sales, marketing, and service; a data storage and back
office applications that may integrate and analyze data about customers. Thus CRM
technology may improve an organization’s ability to sustain profitable customer
relationships by gathering and analyzing information about profitable customers,
24
facilitating more efficient and effective firm-customer interactions, and streamlining
product or service customization. So CRM technology may strength customer-related
capabilities (Day, 2003).
A review of different studies (Chen and Popovich, 2003; Peppard, 2000; Mithas et
al., 2005; Xu and Walton, 2005; Zablah et al., 2004) reveals three aspects of CRM
technology: 1) technologies that are used in the external operation with customers and
facilitate a two-way communication between the firm and its customers; 2) technologies
that are used in internal operations (primarily marketing, sales, and customer service)
aimed at automating and facilitating activities; 3) technologies that act above the other
two technologies and enable firms to analyze data and information and disseminate the
resulting knowledge throughout the organization. This definition is consistent with the
“CRM Techno-Functional” big picture set forth by Greenberg (2004), which can be a
complete definition of CRM technologies. This definition, which is based on META
group segmentation, divides CRM technologies into collaborative, operational, and
analytical categories.
Technologies such as groupware, live boards, argumentation spreadsheets, and rapid
prototyping—to name a few—are but a shadow of what will come as these technologies
merge into collaborative environments. The new generation of collaborative
technologies will have two effects on product development processes: They will enable
many activities to be done that could never have been done before, and they will allow
activities that could be done to be completed much faster, more efficiently, and more
effectively (Peppers and Rogers, 2004).
These technologies reach across customer touch points and can include different
communication means that a customer might interact with, such as e-mail, phone calls,
fax, Web site pages, and so on (Greenberg, 2004). These technological tools are all
mentioned as channels by which firms interact with their customers (Payne and Frow,
2005). They are of much importance because they have the ability to enhance the
customer experience (Payne and Frow, 2004).
25
2.4.2 Collaborative CRM technology in electronic banking sector
According to Peppers and Rogers (2004), firms are capable of making explicit
bargains rather than implicit bargains with interactive communication technologies.
They can interact, one to one, directly with their individual customers, either directly or
through various interactive media vehicles. An explicit bargain is, in effect, a “deal” that
an enterprise makes with an individual to secure the individual’s time, attention, or
feedback. Dialogue and interaction have such important roles to play, in terms of
improving and enhancing a relationship that it is often useful for an enterprise actually
to “compensate” a customer, in the form of discounts, rebates, or free services, in
exchange for the customer participating in a dialogue.
In an interactive medium, an advertiser can secure a consumer’s actual permission
and agreement, individually. By making personal preference information a part of this
bargain, the service can also ensure that the ads or promotions delivered to a particular
subscriber are more personally relevant, in effect increasing the value of the interaction
to the marketer by increasing its relevance to the consumer. By this way, firms can do
“permission marketing” in which customers have agreed, or given their permission to
receive personalized messages.
Technology has helped enterprises to consummate the explicit bargain with
individuals by enabling them to interact with each customer on a personal level. Many
enterprises have used two-way, addressable media as individualized customer
communication channels.
Sophisticated interactive technologies enable enterprises to ensure that their
customer-contact personnel can remember an individual customer and his preferences.
A company can use software that creates an “ecosystem” of data about its customers,
and cull information from all of the touch points where it interacts with customers—call
centers, Web sites, e-mail, and other places. If the enterprise can better understand its
customers, it can better serve them by providing individually tailored offers or
promotions and more insightful customer service (Cross, 2001).
CRM technology is the information technology that is deployed for the specific
purpose of better initiating, maintaining, and/or terminating customer relationships. The
26
potential for information technology to constitute a sustainable competitive advantage
has been amply discussed (Bharadwaj et al., 1993). All banks today have applied
information technology to deploy CRM system. As a major part of CRM
implementation, literature suggests a set of capabilities related to CRM technology
(Croteau and Li, 2003).
Buttle (2004) also stated that ‘multichannel CRM’, whereby customer contact
channels such as sales, partners, marketing and the service centre were consolidated into
a single view of the customer, across all communication media including face-to-face,
voice telephony, e-mail, web and wireless. Multichannel CRM presented a significant
technical challenge. The technology required to support remote field sales people is very
different to the technology required to support a large, high-volume call centre. This
technical challenge made it difficult to build all of the customer channels in one system.
Hence, in e-banking sector, multichannel CRM technology in customer’s view can be
expressed via banking kiosk (ATM/POS), online support channels (such as web,
telephone, mobile phone, web…)
2.4.3 The influence of collaborative CRM technology on CRM performance
Empirical results regarding the performance impact of technology at firm level are
mostly positive (Hitt and Brynjolfsson, 1996). Less attention has been paid to the degree
of usage of information technology in the context of CRM (Jayachandran et al., 2004).
Technology usage is regarded a key driver of organizational success (Devaraj and Kohli,
2003; Mahmood et al., 2001). In research of (Greve and Albers, 2006), CRM
technology usage enable firms to obtain performance gains and include two variables
(CRM system usage, support through CRM system). Figure 9. shows more details:
27
Figure 9. Research model of Performance in CRM
Source: Greve and Albers (2006)
When researched on CRM value generation process, Keramati et al. (2010) stated
that CRM processes were affected by technological CRM resources and infrastructural
CRM resources (see Figure 10).
Figure 10. CRM value generation process
Source: Keramati et al. (2010)
28
Nili (2010) developed resources of technological CRM into three components:
collaborative CRM technologies, operational CRM technologies, and analytical CRM
technologies for electronic banking services. Based on this statements, customers can
evaluate CRM Technology usage through collaborative CRM technologies (telephone
banking, automated teller machine, point of sale, pin pad, internet banking, email
management, mobile banking, SMS banking, help desk, call center) and operational
CRM technologies (information systems in sales activities; information systems in
marketing activities; information systems in customer services and support activities;
credit cards). Thus, collaborative CRM technology in electronic banking services can be
understood types of electronic banking services which customers use.
2.5 Employee-client relationship
2.5.1 The concept of Employee-client relationship
In the paper about performance of personal banking sector, Reed et al. (2009)
mentioned social capital factors as independent factors. Social capital exists either
among employees or between employees and external actors (Edvinsson and Malone,
1997; Nahapiet and Ghoshal, 1998; Pennings et al., 1998; Schroeder et al., 2002;
Stewart, 1997). According Reed et al., external social capital showed social
relationships existing between employees and clients. The customer-employee
connection had also received attention in services marketing literature on commercial
friendships (Price and Arnould 1999) and customer intimacy (Stern, Thompson, and
Arnould 1998).
Ali et al. (2006), Ali and Alshawi (2005) also made mention of cultural dimensions
framework for the management of CRM systems implementation. In this study,
employee-client relationship is developed and also considered as one of cultural
dimensions in customer’s view. In terms of individual relationship aspect, Ali et al.
mentioned cultural dimension of Trompenaars (1993), include universalismparticularism, neutral vs. emotional relationship orientations, and specifi vs. diffuse
orientations.
29
2.5.2 Employee-client relationship in electronic banking sector
Gremler and Gwinner (2000) in a research about service relationship between
customer and employee mentioned the concept ‘customer-employee rapport’. Their
notion of personal connection in a service relationship is based on customer’s
perception of a bond between the two parties. Price et al. (1995) used the phrase
‘authentic understanding’ to describe relational elements in service transaction well
beyond traditional customer contact employee roles. Authentic understanding is
developed when service provider and client engage in self-revelation, expand emotional
energy, and connect as individuals.
In Vietnam electronic banking sector, although banking services are mainly
conducted through electronic channels, customers have trend to contact with the
employee who has already acquaintance with them when encountering obstacle.
2.5.3 The influence of Employee-client relationship on CRM performance
In this study, Employee-client relationship is considered as a cultural aspect of
relationship in Vietnam commercial banks. This is similar to external social capital in
paper of Reed et al. (2009). Furthermore, this cultural dimension was expected have
effects on CRM systems implementation (Ali and Alshawi, 2005; Ali et al., 2006).
Danneels (2003) argued that getting too close to clients may impair innovation and
flexibility as firms emphasize catering to the needs of existing clients at the expense of
seeking out new clients. In spite of this, catering to the needs of existing customers will
enhance CRM performance. Reinartz et al. (2004) suggested that, when potential
customers were establishing a relationship with a company, they preferred being in
contact with people to technology-driven systems. This issue also reflects relationship
between Vietnam banks and electronic banking customers in practice. In Vietnam,
according to author’s experience, this relationship is affected by factor ‘employee-client
relationship’ which belonging to culture.
2.6 Relationship-management assessment tool
From review literature above, perception of customers about operational CRM
(interaction supported by collaborative CRM, customization, communication) and
30
customer-centric culture (employee-client relationship), firms can use an assessment
tool for internal evaluation to estimate their customers’ view in CRM performance (such
as mentioned in study of Lindgreen et al., 2005).
Lindgreen et al. built ten elements to evaluate CRM within business. Each element
ranked in eleven level:
Level 0: Sell goods/services to customers who are willing to buy, have no
criteria in place to select customers.
Level 1: Have a customer strategy to select customers. Someone in organization
is responsible for this strategy.
Level 2: Define customer strategies, which are mainly focused on acquiring new
customers.
Level 3: Base customer strategies primarily on the needs of prospective and
existing customers, rather than on (potential) customer-lifetime value.
Level 4: Analyze the lifetime value of individual customers to understand their
importance to organization. Different approaches including for example activity-based
costing are used to calculate the value of individual customers.
Level 5: Rank customers by their value in order to define customer segments.
Customers with similar lifetime value are allocated to the same customer segment.
Level 6: Set clear business objectives for each customer segment. Develop a
corresponding value proposition that is consistent with these objectives including. In
each segment customers have the same lifetime value, but are differentiated from each
other by their needs.
Level 7: Build and develop relationships with most value customers, continually
analyze their potential, and take actions to transform unprofitable customers into
profitable ones.
Level 8: Retain most valuable customers by understanding loyalty drivers and
by introducing appropriate value-adding propositions. Know why some customers
defect and how to win these customers back. Increase customer retention by offering
value-adding propositions.
31
Level 9: Meet the specific needs of customers, and firm’s value propositions
regularly exceed their expectations. Build unique relationships with most valuable
customers. Customers prefer organization to do business with rather than direct
competitors because firm excellent in creating value-adding opportunities, review
customer strategy continually.
Level 10: Develop excellent customer strategies, which create customer trust and
commitment, and drive the growth in firm’s profitability. Firm is the number one
strategic supplier of their most valuable customers. In order to develop the most valueadding goods/services in the marketplace firm collaborative closely with customers to
exchange knowledge.
Toward this approach, element ‘information technology’ can be used to estimate
‘collaborative CRM technology’ which evaluated by customers. Similarly, element
‘Customer-interaction strategy’ can be used to estimate ‘Two way communication’,
element ‘Customer strategy’ can be used to estimate ‘Customization’, element ‘Culture’
can be used to estimate ‘Employee-client relationship’.
32
Chapter 3. RESEARCH METHODOLOGY
3.1 Research design
Research design is the plan and structure of investigation so conceived as to obtain
answers to research questions. The plan is the overall scheme or program of research. It
includes an outline of what the investigator will do from writing the hypotheses and
their operational implications to the final analysis of the data (Kerlinger, 1986).
Research design enables the researcher to select appropriate methods in order to meet
the research objectives in the most efficient way.
To answer the research questions, four variables has been considered and
investigated. These factors are expected have influences on CRM performance. Hence,
this is quantitative study with deductive approach.
According to Hair et al. (2007), the data in quantitative approach are numbers and
lends itself to statically analysis in order to imply the characteristics of something. This
approach provides objectivity because the respondents are the ones who provide the
numbers, so researcher’s opinion does not have any impact on testing the hypothesis.
This approach is often used in explanatory researches. In addition, it allows
generalization and enables the researcher to predict the future.
3.2 Research purpose
3.2.1 Exploratory research
An exploratory study is undertaken when not much is known about the situation at
hand, or no information is available on how similar problems or research issues have
been solved in the past. In such cases, extensive preliminary work needs to be done to
gain familiarity with the phenomena in the situation, and understand what is occurring,
before we develop a model and set up a rigorous design for comprehensive investigation.
Hence, exploratory research is undertaken to better comprehend the nature of problem
sine very few studies might have been conducted in that area. Extensive interviews with
many people might have to be conducted to get a handle on the situation and understand
the phenomenon.
33
Some qualitative studies (as opposed to quantitative data gathered through
questionnaire) where data reveal some pattern regarding the phenomenon of interst,
theories are developed and hypotheses formulated for subsequent testing.
Exploratory studies are also necessary when some facts are known, but more
information is needed for developing a viable theoretical framework. In general,
exploratory research is important for obtaining a good grasp of the phenomenon of
interst and advancing knowledge through subsequent theory building and hypothesis
testing (Sekaran and Bougie, 2009).
3.2.2 Descriptive research
A descriptive research is undertaken in order to ascertain and be able to describe the
characteristics of the variables of interest in a situation. Quite frequently, descriptive
studies are undertaken in organizations to learn about and describe the characteristics of
a group of employees, or to understand the characteristics of organizations that follow
certain common practices
The goal of a descriptive study, therefore, is to offer to the researcher a profile or to
describe relevant aspects of the phenomenon of interest from an individual,
organizational, industry-oriented, or other perspective. In many cases, such information
may be vital before even considering certain corrective steps, should the organization
consider changing its practices.
Descriptive studies thus become essential in many situations whereas qualitative
data obtained by interviewing individuals may help the understanding of phenomena at
the exploratory stages of a study, quantitative data in terms of frequencies, or mean and
standard deviations, become necessary for descriptive studies (Sekaran and Bougie,
2009).
3.2.3 Explanatory research
Explanatory researches study a problem or a situation in order to finding out the
relationship between dependent and independent variables. This kind of research is the
most appropriate design to test whether one variable causes or determines the value of
another (Hair et al., 2007). Sekaran and Bougie (2009) also called explanatory research
is hypothesis testing which is undertaken to explain the variance in the dependent
34
variable or to predict organizational outcomes. It can be done with both qualitative and
quantitative data.
For the research purposes mentioned in this study, it can be concluded that this study
is mainly explanatory. Beside this, questionnaire is used to collect data and hypothesis
testing is conducted, so this is explanatory.
3.3 Research model
Base on literature review, the thesis creates the hypothesis as following (Figure 11):
Collaborative
technology
Two way
communication
H1 +
H2 +
CRM
performance
H3 +
Customization
H4 +
Employee-client
relationship
Figure 11. Determinants of CRM performance in e-banking individual customer’s
view
3.4 Generation of hypotheses
Hypotheses:
H1: Collaborative technology has positive relationship with CRM performance
of electronic banking services
H2: Two way communication has positive relationship with CRM performance
of electronic banking services
H3: Customization has positive relationship with CRM performance of
electronic banking services
H4: Employee-client relationship has positive relationship with CRM
performance of electronic banking services
35
3.5 Data collection method
Zikmund (2000) and Sounders et al. (2009) say there are two classifications for
collected data which are: primary and secondary data. Primary data can be collected for
instance through interview, observation, and questionnaire. On the other hand,
secondary data is the information collected from the studies done before and can be
collected from the Internet or libraries.
Sounders et al. (2009) state that questionnaire is one of the most widely used
techniques to collect data within the survey strategy and since each respondent answers
the same set of questions, it is an efficient technique of gathering responses from a large
sample.
For this study, primary data seems to be the most suitable one and questionnaire is
used as the instrument to collect the primary data in this research. A questionnaire is a
reformulated written set of questions to which respondents record their answers, usually
within rather closely defined alternatives. Questionnaires are an efficient data collection
mechanism when the researcher knows exactly what is required and how to measure the
variables of interest. In this study, questionnaires have been distributed to customers
who used/be using electronic banking services. To increase the response rate, meeting
was the first method to give the questionnaires. Because of the limitation of time and
accessibility, some of the respondents received the questionnaires through e-mail along
with an explanation letter.
3.6 Sampling
This study uses non-probability sampling technique. In this method, the probability
of each element in the population is not known and the selected sample is not
necessarily representative of the population statistically. So, to select the elements in the
sample the researcher uses expert judgment, experience, and convenience. Therefore,
unlike probability samples, the results cannot be generalized to the population (Hair et
al., 2007). These authors also discuss that the most common types of non-probability
36
sampling techniques include Convenience sampling, Judgment sampling, Snowball
sampling, Self selection sampling, and Quota sampling.
Population of this study is electronic banking customers of Vietnam commercial
banks. Danang is the city in the middle of Vietnam, has the similarities with other cities
which have most e-banking customers (such as Hanoi, Ho Chi Minh city…). Hence,
Danang was chosen for sampling.
As mentioned above, time limitation and sample includes electronic banking
customers in Danang city, this research chooses non-probability technique with
convenience sampling. According to Bollen (1989), minimum of sample size is five
samples for one measured item. In this study, pilot test was conducted within one week
(from 10th to 18th in December, 2012). After that, there are 400 questionnaires issued
and 356 questionnaires returned (from December, 2012 to February, 2013). There are
344 questionnaires inputted for data analysis.
3.7 Measurement instrument
Measurement of components in research model shown in Table 3. After pilot test,
the remaining variables are shown in Table 4.
Table 3. Measurement of components
Variables
Items
CRM performance (Rootman, 2006)
Bank’s success concern High concern for bank’s success
Confidence
Confidence in bank
Strong bond
Strong bond with bank
Benefits
Receive many benefits
Commitment
Committed completely with bank
Two way communication (Rootman, 2006)
Account statement
Receiving account statements from bank
Receiving banking information through various media
Media
regularly
Senior managers are available for appointment when
Senior managers
necessary
Efficiency
Bank’s employees communicate effectively
Meeting
Being invited to client meeting
Available
Receive information if certain services will be available
37
Customization (Nili, 2010)
Individual message
Individualization of marketing messages
Services customization Customization of services
Desired communication Communicating with customers based on desired way
Collaborative CRM Technology (Nili, 2010)
Using banking kiosk (automated teller machine/payment
Banking kiosk
cards at point of sales)
Using online support channel (Mobile banking/ Call center/
Online support channel
Website)
Email
Using e-mailing
Internet banking
Using internet banking service
Employee-client relationship (Trompenaars, 1993; Reed et al, 2009)
Particularism
Situation-specific relationship obligations and unique
(Trompenaars, 1993)
circumstances
Emotional Orientation
Interactions where emotions is readily expressed
(Trompenaars, 1993)
Diffusion Orientation
Business relationships in which private and work
(Trompenaars, 1993)
encounters are not demarcated and ‘segregated-out’
Regularly visit
Employees visit customers regularly
(Reed et al., 2009)
Visit accompanied by
Employees visit customers accompanied by the bank’s top
managers (Reed et al.,
managers
2009)
Final questionnaire was designed based on 18 remaining items. There were 400
questionnaires issued and 356 questionnaires returned back.
Table 4. Measurement of components after pilot test
Dimensions
Collaborative CRM
technology
(4 items)
2-way communication
(3 items)
Customization
(3 items)
Variables
Banking kiosk
Online support channel
Email
Internet banking
Media
Meeting
Available
Individual message
Services customization
Desired communication
38
Code
Tech1
Tech2
Tech3
Tech4
Com1
Com2
Com3
Cus1
Cus2
Cus3
Employee-client relationship
(3 items)
CRM performance
(5 items)
Acquaintance
Regularly visit
Visit accompanied by managers
Bank’s success concern
Confidence
Strong bond
Benefits
Commitment
Rel1
Rel2
Rel3
Perf1
Perf2
Perf3
Perf4
Perf5
The study also applied CRM assessment tool of Lindgreen et al. (2005) for internal
evaluation about operational CRM and customer-centric culture of two banks –
Vietcombank and BIDV. This aims to compare results between perceptions of banks
and customers about CRM performance (see questionnaire in Appendix A).
Interval scale
An interval scale allows researchers to perform certain arithmetical operations on the
data collected from the respondents. The interval scale lets us measure the distance
between any two points on the scale. This helps us to compute the means and the
standard deviations of the responses on the variables. In other words, the interval scale
not only groups individuals according to certain categories and taps the order of these
groups; it also measures the magnitude of the differences in the preferences among the
individuals (Sekaran and Bougie, 2009).
For these reasons, variables in the research model will use 5-point Likert scale – a
type of interval scale, within: (1) strongly disagree, (2) disagree, (3) no comments, (4)
agree, and (5) strongly agree.
3.8 Pilot test
The study conducts a pilot test in order to evaluate the respondents' comprehension
of the questionnaire and estimate the average time to complete it. Firstly, the 1st draft
questionnaire was translated to Vietnamese. This form of the questionnaire was
distributed to six experts in banking industry with high creditability in Danang city.
Secondly, 2nd draft questionnaire was delivered to 32 participants to test again.
From the results, there will be some modifications that should be performed for
some questions. In addition, the wording and relevancy of questions will be checked
39
and based on that, some other questions will be modified, too. There were 23 items used
for measuring all components in research model. But there were 5 items removed out
after pre-test. These items were included in the variable ‘Two way communication’ and
variable ‘Employee-Client relationship’.
Toward ‘Two way communication’ dimension, three variables were ruled out:
Account statement, Available managers, Efficiency. Some banking experts said that in
e-banking sector, ‘Receiving account statements from bank’ is not significant to
relationship between customers and banks because they will request account statement
when they need. Experts also added that in e-banking sector, it is not important if senior
managers are available or not. The item ‘Bank’s employees communicate effectively’
also makes respondents not easy to understand. Furthermore, the item ‘Communicating
with customers based on desired way’ also reflects efficiency of communication, so
‘efficiency’ should be extracted from the scale. In the research of Rootman (2006), the
item which measured ‘Account statement’ was also removed because of weak factor
loading coefficient, two items which measured ‘Available managers’ and ‘Efficiency’
after factor analysis were transformed into other variables.
In terms of ‘Employee-client relationship’ dimension, the variables ‘Emotional
Orientation’ and ‘Diffusion Orientation’ were also not appropriate to measure this
component. These two variables were difficult to understand by customers and experts
stated that they were also not suitable for this scale.
3.9 Testing goodness of data
According Hair et al. (2010), the researcher’s goal of reducing measurement error
can follow several paths. In assessing the degree of measurement error present in any
measure, the researcher must address two important characteristics of a measure:
3.9.1 Reliability
The reliability of a measure is established by testing for both consistency and
stability. Consistency indicates how well the items measuring a concept hang together
as a set. The researcher should always assess the variables being used and, if valid
40
alternative measures are available, choose the variable with the higher reliability
(Sekaran and Bougie, 2009).
According to Hair et al. (2007), if the repeated application of a survey instrument
results in consistent scores, we can consider it reliable. In other words, a research can be
considered reliable, if its measuring procedure yields the same results on repeated trials
(Saunders et al., 2009).
Cronbach’s alpha is a reliability coefficient that indicates how well the items in a set
are positively correlated to one another. This coefficient is computed in terms of the
average intercorrelations among the items measuring the concept. The closer cronbach’s
alpha is to 1, the higher the internal consistency reliability (Bob E.Hayes, 1983; Sekaran
and Bougi, 2009).
Cronbach alpha coefficient is used to reject “trash” items which have corrected item
total correlation smaller than 0.3. Scale will be chosen if this coefficient greater than 0.6
(Nunnally and Bernstein, 1994). According Yockey (2007), significance of Cronbach
alpha as follows:
 below or equal to 0.59:
Poor
 = 0.60 – 0.69:
Marginal
 = 0.70 – 0.79:
Fair
 = 0.80 – 0.89:
Good
 = 0.90 and above:
Excellent
3.9.2 Validity
Validity is the degree to which a measure accurately represents what it is supposed
to. Ensuring validity starts with a thorough understanding of what is to be measured and
then making the measurement as “correct” and accurate as possible. However, accuracy
does not ensure validity. Factorial validity can be established by submitting the data for
factor analysis. The results of factor analysis will confirm whether or not the theorized
dimensions emerge.
To ensure the validity of this research, the approaches mentioned below have been
adopted:
41
-
To make sure that the measurement scales were adapted appropriately, the
questionnaire has been translated into Vietnamese.
-
The questionnaire will be reviewed by the supervisors of this work and banks
experts to remove and correct the potential problems before sending it to the
respondents (content validity).
-
To check the construct validity of the questionnaire and also to find out if all
indicators of each variable (construct) measure what is expected, 'exploratory
factor analysis' will be used. This result continue be used for ‘confirmatory
factor analysis’ to ensure proper of the model. The calculations for this section
lead to satisfactory results.
Factor analysis, including both principal component analysis and common factor
analysis, is a statistical approach that can be used to analyze interrelationships among a
large number of variables and to explain these variables in terms of their common
underlying dimensions (factors). The objective is to find a way of condensing the
information contained in a number of original variables into a smaller set of variables
(factors) with a minimal loss of information. By providing an empirical estimate of the
structure of the variables considered, factor analysis becomes an objective basis for creating summated scales.
Stage 1: Objectives of factor analysis
Factor analysis can identify the structure of a set of variables as well as provide a
process for data reduction. In this study, for example, perception of independent metric
variable ‘employee-client relationship’ on 5 attributes is examined for the following
reasons:
-
Understand whether these perception can be “grouped”. Even the relatively
small number of perception examined here presents a complex picture of
separate correlations.
-
Reduce the 5 variables to a smaller number. If the 5 variables can be represented
in a
smaller number of composite variables, then the other multivariate
techniques can be made more parsimonious
Stage 2: Designing a Factor analysis
42
Understanding the structure of the perception of variables requires R-type factor
analysis and a correlation matrix between variables, not respondents. All the variables
are metric and constitute a homogeneous set of perceptions appropriate for factor
analysis.
Stage 3: Assumption in Factor analysis
The underlying statistical assumption influence factor analysis to extent that they
affect the derived correlation. Departures from normality, homoscedasticity, and
linearity can diminish correlation between variables. The first step is a visual
examination of the correlation, identifying those that are statistically significant.
Anti-image correlation matrix is matrix of the partial correlations among variables
after factor analysis, representing the degree to which the factors explain each other in
the results. The diagonal contains the measures of sampling adequacy for each variable,
and the off-diagonal values are partial correlations among variables.
Overall significance of the correlation matrix can be assessed with the Barlett test
and the factorability of the overall set of variables and individual variables using the
measure of sampling aquadecy (MSA). Validity of factor analysis and sample data is
tested through Kaiser-Meyer-Olkin (KMO) index. Kaiser-Meyer-Olkin (KMO) measure
of sampling adequacy is an index to consider the validation of factor analysis. KMO
must be large enough (between 0.5 and 1), means factor analysis is valid (Garson, 2003),
if this index smaller than 0.5, factor analysis can be not valid to data.
Stage 4: Deriving factors and assessing overall fit
The reduced correlation matrix with communalities on the diagonal was used in the
common factor analysis. Principal Axis factoring with promax rotation (oblique) will
reflect data structure more exactly than principal components with varimax rotation
(orthogonal) (Gerbing and Anderson, 1988). Principal Axis factoring extraction method
will give us minimum components to explain general variance of items in interaction
between them. While principal components method will give us result with set of
components that explains both variance and specific of them. But with single trend scale,
principal components extraction method is more properly.
43
In addition, number of components is confirmed based on Eigenvalue. Items with
eigenvalue smaller than 1 is rejected from model (Garson, 2003) and variance explained
criteria is total variance explained must be greater than 50%. To scale attain converge
value, single correlation coefficients between items (factor loading) must greater than
0.5 within one component (Jun et al., 2002). To attain distinguish value between items,
factor loading must greater than or equal to 0.3 (Jabnoun et al., 2003).
Stage 5: Interpreting the factors
With factors to be analyzed, the study turns to interpreting the factors by using factor
matrix of loadings. The interpretation process then proceeded by examining the
unrotated and after that rotated factor matrices for significant factor loadings and
adequate communalities. If deficiencies are found, respecification of the factors is
considered. Once the factors are finalized, they can be described based on significant
factor loadings characterizing each other.
Stage 6: Validation of factor analysis
Validation of any factor analysis result is essential, particularly when attempting to
define underlying structure among the variables. We must look to other means, such as
split sample analysis or application to entirely new samples.
Confirmatory factor analysis (CFA) is also used to test the validity of data. It deals
specifically with measurement dodels, that is, the relationships between observed
measures or indicators (e.g, test items, test scores, behavioral observation ratings) and
latent variables or factors. A fundamental feature of CFA is its hypothesis-driven nature.
It is unlike its counterpart, exploratory factor analysis (EFA), in that the researcher
mush prespecify all aspects of the CFA model. Thus, the researcher must have a firm a
priori sense, based on past evidence and theory, of the number of factors that exist in the
data, of which indicators are related to which factors, and so forth. CFA has become one
of the most commonly used satistical procedures in applied research. This is because
CFA is well equipped to address the types of questions that researchers often ask. CFA
is an indispensable analytic tool for construct validation in the social and behavioral
sciences. The results of CFA can provide compelling evidence of the convergent and
discriminant validity of theoretical constructs (Brown, 2006).
44
When using CFA, some indices should be considered:
Chi-Square (χ2) test indicates the difference between observed and expected
covariance matrices. Value closer to zero indicate a better fit (Gatignon, 2010). This is
difficult to occur so that chi-square relative to its degree of freedom (χ2 /df) often be
used to evaluate the validity. Some authors supposed that this index should be 1 < χ2/df
< 3 (Hair et al., 1998).
The root mean square error of approximation (RMSEA) avoid issues of sample size
by analyzing the discrepancy between the hypothesized model (Hooper et al., 2008).
The RMSEA ranges from 0 to 1, with this value smaller than 0.08 is indicative of
acceptable model fit (Taylor et al., 1993).
Some other indices such as Goodness of fit index (GFI), Comparative fit index
(CFI)… greater than 0.9 is indicative of suitable model (Segar and Grover, 1993; Chin
and Todd, 1995).
3.9.3 Dependence technique
When considering the application of multivariate statistical techniques, the answer to
the first question – Can the data variables be divided into independent and dependent
classifications? – indicates whether a dependence or interdependence technique should
be utilized. In Figure 12, the dependence techniques are on the left side and the
interdependence techniques are on the right.
45
Figure 12. Selecting a Multivariate Technique
Source: Hair et al. (2010)
A dependence technique may be defined as one in which a variable or set of variables is identified as the dependent variable to be predicted or explained by other
variables known as independent variables. An example of a dependence technique is
46
multiple regression analysis. In contrast, an interdependence technique is one in which
no single variable or group of variables is defined as being independent or dependent.
Rather, the procedure involves the simultaneous analysis of all variables in the set.
Factor analysis is an example of an interdependence technique.
The different dependence techniques can be categorized by two characteristics: (1)
the number of dependent variables and (2) the type of measurement scale employed by
the variables. First, regarding the number of dependent variables, dependence
techniques can be classified as those having a single dependent variable, several
dependent variables, or even several dependent/independent relationships. Second,
dependence techniques can be further classified as those with either metric
(quantitative/numerical) or nonmetric (qualitative/categorical) dependent variables. If
the analysis involves a single dependent variable that is metric, the appropriate
technique is either multiple regression analysis or conjoint analysis. Conjoint analysis is
a special case. It involves a dependence procedure that may treat the dependent variable
as either nonmetric or metric, depending on the type of data collected. In contrast, if the
single dependent variable is nonmetric (categorical), then the appropriate techniques are
multiple discriminant analysis and linear probability models (Hair et al., 2010).
In context of this thesis, there is one dependent variable – CRM performance which
is measured as metric scale. The four remaining variables are independent variables,
included three metric variables (two way communication, customization, and employeeclient relationship) and one non-metric variable (collaborative CRM technology). So
multiple regression analysis will be used.
Cohen et al. (2002) examines power for most statistical inference tests and provides
guidelines for acceptable levels of power, suggesting that studies be designed to achieve
alpha levels of at least .05 with power levels of 80 percent To achieve such power levels,
all three factors—alpha, sample size, and effect size—must be considered
simultaneously.
Multiple regression is the appropriate method of analysis when the research
problem involves a single metric dependent variable presumed to be related to two or
more metric independent variables. The objective of multiple regression analysis is to
47
predict the changes in the dependent variable in response to changes in the independent
variables. This objective is most often achieved through the statistical rule of least
squares (Hair et al., 2010). Toward this study, multiple regression is useful.
The coefficient of determination, R2, provides information about the goodness of fit
of the regression model: it is a statistical measure of how well the regression line
approximates the real data points. R2 is the percentage of variance in the dependent
variable that is explained by the variation in the independent variable.
Multicollinearity also be considered to test the ability of an additional independent
variable to improve the prediction of the dependent variable is related not only to its
correlation to the dependent variable, but also to the correlation(s) of the additional
independent variable to the independent variable(s) already in the regression equation.
Collinearity is the association, measured as the correlation, between two independent
variables. Multicollinearity refers to the correlation among three or more independent
variables (evidenced when one is regressed against the others). Although a precise
distinction separates these two concepts in statistical terms, it is rather common practice
to use the terms interchangeably.
The simplest and most obvious way to detect multicollinearity is to check the
correlation matrix for the independent variables. The presence of high correlations
(most people consider correlations of 0.70 and above high) is a first sign of sizeable
multicollinearity. However, when multicollinearity is the result of complex relationships
among several independent variables, it may not be revealed by this approach. More
common measures for identifying multicollinearity are therefore the Tolerance value
and the Variance Inflation Factor (VIF – the inverse of the Tolerance value). These
measures indicate the degree to which one independent variable is explained by the
other independent variables. A common cutoff value is a tolerance value of 0.10, which
corresponds to a VIF of 10 (Sekaran and Bougi, 2009).
48
Chapter 4. DATA ANALYSIS AND FINDINGS
This chapter aims to analyze the collected data in order to systematically present the
descriptive findings of the research study, to interpret significance of these findings as
results of data analysis, to present the results of testing the model and to explain how the
model developed from a literature review was supported by data analysis. The collected
data was converted into valuable information by using statistical tests. This chapter is
drawn up in two parts: descriptive and interferential statistics.
4.1 Descriptive Statistics
In this section, the customer information related to banks which its e-banking
services have been using by customers, how long they being customers of these banks,
purpose of usage, and demographic data (age, gender, educational level, monthly
income).
4.1.1
Customers’ bank statistics
There are 344 respondents but the number of banks they use their e-banking services
up to 615. In real, one customer can maintain transactions with various banks. As can be
seen, customer quantity of Dong A bank is up to 32.5%. This shows development of
Dong A bank’s market in Danang city. This may be a result of card policies with
business customer of this bank. The following banks are Vietcombank (14.5%),
Agribank (14.1%), Vietinbank (10.7%), BIDV (9.9%). These four banks were stateowner banks in the past. In spite of this, the respondents were requested only choose
one bank which its e-banking services they often use to answer.
Table 5 shows in detail time and purpose of usage of respondents. Almost customers
use e-banking services because two purpose (private and work) and their using time
above one year. Customers of two big commercial banks – Agribank and Vietcombank
are mainly long year customers (above 3 years). Dong A bank in spite of has big
amount but within one to three years.
49
Table 5. Purpose and Time of usage
Purpose of usage
Time of usage
Under 1
year
Customer's bank
name
Private
Customer's bank
name
Total
7
4
11
Vietcombank
3
3
6
Techcombank
2
1
3
ACB
1
0
1
Dong A bank
8
12
20
Vietinbank
0
2
2
BIDV
1
3
4
Sacombank
0
1
1
Others
1
2
3
12
15
27
Agribank
17
8
15
40
Vietcombank
14
5
18
37
Techcombank
2
4
3
9
ACB
1
2
4
7
Dong A bank
37
12
58
107
Vietinbank
14
5
13
32
BIDV
9
2
20
31
Sacombank
2
1
4
7
Others
4
4
8
16
53
83
23
159
Agribank
11
1
24
36
Vietcombank
12
3
31
46
Techcombank
0
1
9
10
ACB
3
0
5
8
17
5
51
73
Vietinbank
4
1
27
32
BIDV
7
2
17
26
Total
Above 3
years
Both
Agribank
Total
From 1-3 Customer's bank
year(s)
name
Work
Dong A bank
50
Sacombank
1
1
10
12
Others
7
0
28
35
37
9
112
158
Total
4.1.2 Demographic characteristics
Table 6 expresses the frequency of the respondents’ demographic characteristics. As
can be shown, almost respondents are young (78.8% are between 18 to 35, 16.3% are
between 36 to 45) and the ratio of male and female is equivalent. Almost respondents
are at university level and higher (61.6% and 27.3% respectively); only 11%
respondents are at high school level and intermediate school/colleges level. In spite of
almost respondents have bachelor or equivalent, there are 55.2% respondents have
monthly income just below 5 million VND.
Table 6. Respondents’ demographic characteristics
Frequency
Percent
Age
Under 18
18-35
36-45
46-60
4
271
56
13
1.2
78.8
16.3
3.8
Gender
Male
Female
174
170
50.6
49.4
Education level
High school
Intermediate school/Colleges
University
Higher
10
28
212
94
2.9
8.1
61.6
27.3
190
122
25
6
1
55.2
35.5
7.3
1.7
.3
Monthly income Below 5 mil VND
5-10 mil VND
10-18 mil VND
18-32 mil VND
Above 32 mil VND
4.1.3 Mean of five research variables
The mean of five research variables are represented in table 7. (the scores are out of
5). All of the scales in the questionnaire were measured using 5-point Likert scales and
51
almost the scores in table are more than the average score (3), except Employee-Client
relationship has Mean value lower a bit than average score. This is new variable built in
CRM performance model in Vietnam, so that this value can be acceptable. In addition,
there are so many people open credit cards because of commitment between their
organizations and banks. These customers may not choose e-banking services in
initiative way.
Table 7. Descriptive statistics for independent and dependent variables
N
Minimum
Maximum
Mean
Std. Deviation
Collaborative technology
344
1.50
5.00 3.1156
.82635
Communication
344
1.00
5.00 3.0979
.92395
Customization
344
1.00
5.00 3.5455
.81084
Relationship
344
1.00
5.00 2.5911
1.02295
CRM performance
344
1.00
5.00 3.2215
.71151
Valid N (listwise)
344
4.2 Assessing measurement scale
As mentioned in chapter three, Cronbach Alpha coefficient scores were calculated in
order to assess the internal reliability of the measuring instrument. Cronbach Alpha
coefficient scores of factors with a value more than 0.6, as recommended by Nunnally
and Bernstein (1994), will be acceptable to use for further analysis. The Cronbach
Alpha coefficient scores of each variables will be presented as Table 8.
According to table 8, all the items are proper for next analysis because Cronbach’s
Alpha of each factor is greater than 0.6. In spite of this, corrected item-total correlation
of banking kiosk is too small (-0.062<0.3) and Cronbach Alpha if this item deleted
higher than current Cronbach Alpha coefficient so Tech1 (Extent of using banking kiosk)
item should be removed in further analysis. After deleting item this item, all of the items
loading on the Collaborative CRM Technology factor contributed to a Cronbach’s
Alpha coefficient score of 0.775, greater than first calculated score. The final scale of
this factor will have three items which are Tech2, Tech3, Tech4.
52
The Cronbach Alpha coefficient of Employee-Client relationship is 0.789. Table 8
also represents that if we take out item 1-Your relationship based on particular
acquaintance (relatives or friends…), our alpha will go up and become 0.857>0.789. In
spite of this, we would not take out this item for two reasons. First, our alpha is above
0.7 so we do not have to take any remedial actions. Second, if we took item 1 out, the
validity of our measure would probably decrease. So all of these items needed to be
included in the next analysis.
Table 8. Reliability statistics for all items
Measure
Cronbach’s
Alpha
Scale Mean
Scale
Corrected
Cronbach's
if Item
Variance if Item-Total Alpha if Item
Deleted Item Deleted Correlation
Deleted
Collaborative CRM Technology
Tech1 (removed later)
.631
8.11
10.620
-.062
.775
Tech2
9.38
6.277
.557
.453
Tech3
10.10
5.567
.587
.413
9.79
5.146
.579
.415
5.83
6.77
5.99
4.038
3.983
3.804
.462
.483
.578
.655
.629
.508
7.17
7.07
7.03
3.176
2.829
3.025
.476
.604
.511
.685
.525
.643
5.36
4.99
5.19
5.410
4.420
4.055
.483
.678
.743
.857
.659
.581
12.71
12.74
12.98
12.81
13.19
8.772
8.560
8.641
8.760
8.572
.438
.531
.569
.481
.413
.693
.657
.646
.676
.708
Tech4
Two way communication
Com1
.691
Com2
Com3
Customization
Cus1
.711
Cus2
Cus3
Employee-Client relationship
Rel1
.789
Rel2
Rel3
CRM performance
Perf1
.723
Perf2
Perf3
Perf4
Perf5
53
` After testing reliability of all scales, there is only one item (Tech1) removed. It can
be seen that ‘Extent of using ATM or POS’ is not the element which has influence on
CRM performance. The reason could be that in Vietnam when customers want to use ebanking services, they must have a credit card first. But opening this kind of card
doesn’t mean that they really perceive extent of Collaborative Technology used by their
banks. So extent of using cards through banking kiosk (ATM/POS) may be not
appropriate in researching CRM performance in view of customers.
4.3 Validity of data
4.3.1 Explanatory factor analysis
The next step in analyzing data is using Exploratory Factor Analysis (EFA) method.
This aims to continuously purify the measurement scales by reducing from a large
number of variables to a minimum number that can explain most of characteristics of
the original variables. Principal component analysis is used for both independent and
dependent variables. It should be noted that since number of factors, total variance
explained, and communalities of the questions can be gained from factor analysis, so the
study aims at calculating the communalities and deleting the questions with little
communalities. This step is also for more preparation to do the confirmatory factor
analysis.
4.3.2 Validity of independent variables
The first EFA result
After conducting exploratory factor analysis for all independent variables, KMO of
all independent items is greater than 0.7 and Barlett’s test significance number is less
than 0.05 (sig. = .000<0.05), it can be said that the data is proper for doing factor
analysis. All the questions related to independent variables are also proper in the process
of factor analysis, no question is deleted (communalities are greater than 0.4).
54
The total variance explained shows that these questions totally form three factors
and these three factors explain and cover about 61.99% of the variance of independent
variables.
The Rotated Component Matrix shows that there are only three components
extracted. This can be caused by Com2-Often be invited to take part in client meeting.
This item has factor loading number smaller than 0.5 (0.485). So that item Com2 should
be eliminated from the model (see more in Appendix B).
The second EFA result
Doing factor analysis again (after eliminating item Com2), the KMO of all
independent variables is also greater than 0.7 (0.768) with Barlett’s test significance
number is less than 0.05 (sig. = .000<0.05), so that the data is proper for doing factor
analysis. The total variance explained shows that these questions totally form four
factors and these four factors explain and cover about 70.34% of the variance of
independent variables (see more in Appendix B).
Table 9. Rotated Component Matrix of Independent variables
Component
Collaborative CRM Employee-Client
Two way
Technology
relationship
Customization communication
Tech4
.871
Tech3
.819
Tech2
.732
Rel3
.889
Rel2
.868
Rel1
.685
Cus2
.869
Cus1
.754
Cus3
.600
Com3
.822
Com1
.718
Cronbach’s
0.775
0.789
55
0.711
0.629
The second EFA shows that all independent variables keep as original items (see
Table 9). Factor 1 – Collaborative Technology included Tech4-Extent of using internet
banking, Tech3-Extent of using email, and Tech2-Extent of using online channel
(Mobile phone/Call Center/Website).
Factor 2 consisted of three items originally expected to measure the variables
Employee-Client relationship.
Factor 3 – Customization included the item Cus2-Customize electronic banking
services, Cus1- Receive individualize marketing messages, Cus3-Be communicated
based on the way customer desires.
Factor 4 – Two way communication included two remaining items: the item Com3Be informed when certain services will be available or not regularly, and the item
Com1- Be informed new or important banking information through various media
regularly. Reliability of this scale is also proper because Cronbach’s Alpha coefficient =
0.629 when tested again.
4.3.2.1 Validity of dependent variables
CRM performance was measured by five items, KMO is greater than 0.7 (0.770) and
Barlett’s Test number is less than 0.05 (sig. = 0.000) so it can be said the data is proper
for doing analysis. Communalities of all items are also greater than 0.4, except item
Perf5-Commit to bank completely (0.379) (see Appendix B). This may lead to the total
variance explained only explains and cover about 48.16% of the variance ‘CRM
performance’.
After deleting item Perf5-Commit to bank completely, the issues as mentioned
above are deal with again. KMO now is equal to 0.717, still greater than 0.7 and has
significant meaning (0.000) so data is proper for doing analysis. Communalities of four
remaining items, these numbers are more than 0.4, are suitable for factor analysis. These
items totally form one factor explain and cover about 53.66% of the variance of ‘CRM
performance’, so the power of explanation of the questions have risen and acceptable.
Perhaps in e-banking sector, almost users want to manipulate by themselves instead of
committing their operations to bank completely.
56
Table 10. Component Matrix of dependent variable
Component
1
Perf1
.779
Perf2
.769
Perf3
.693
Perf4
.684
Table 10. represents that there is only one component extracted. Testing Cronbach
Alpha coefficient again we can see ‘CRM performance’ component has Cronbach’s
Alpha > 0.6 (0.708) and this number is highest, all correlated item-total correlations are
also greater than 0.3.
4.3.2 Confirmatory factor analysis
After EFA, we confirm this result with confirmatory factor analysis, CFA. We
compete two models each other by Lisrel 8.5.1. One is null model which has all
measurable indicators under only one single factor, the other one is first-order factor
model. This first-order factor model divides those measurable indicators into several
groups by the result of EFA. These 15 indicators of CFA are reduced from 18 indicators
of original data by EFA. We test two models of these four groups of independent
variables and one group of dependent variable. The first-order factor model fits the data
better than the null model. The null model has Chi-sqr/df =8.36 , NFI=0.59, GFI=0.77,
AGFI=0.70, RMSEA=0.147 which all are not qualified with good fitness. On other
hand, the first-order factor model has Chi-sqr/df =2.46, NFI=0.88, GFI=0.93,
AGFI=.089, RMSEA=0.065 which fit data much better than the null one, as shown in
table 11 and figure 13, figure 14. Thus, the result of EFA is robust by CFA.
Table 11. Result of confirmatory factor analysis
Model
Null model
First-order factor
(correlated)
Path analysis
Suggested value
Chi-sqr(df)
752.84(90)
197.2 (80)
Chi-sqr/df
8.36
2.46
NFI
0.59
0.88
GFI
0.77
0.93
AGFI
0.70
0.89
RMSEA
0.147
0.065
344.15(125)
smaller, better
2.75
<3
0.88
>0.9
0.93
>0.9
0.89
>0.9
0.065
<0.8
57
Concepecual diagram of null model
Estimates of null model
T-value of null model
Figure 13. The null model
58
Concepecual diagram of
Estimates of first-order factor model
first-order factor model
T-value of first-order factor model
Figure 14. The first-order factor model
59
4.4 Multiple regression analysis
4.4.1 Regression equation
After testing reliabilities of scales and using factor analysis, relationship between
independent variables and dependent variable can be represented in a regression
equation as follow:
YCRMperfomance(i) = bo + b1 *Tech + b2 *Com + b3 *Cus + b4 *Rel + ε(i)
Within equation:
bo is constant
bi is coefficient of independent variable
ε(i) is error term
4.4.2 Testing the conformity of research model
According to ANOVA table (appendix B), the F value is 61.159 and the observed
significance level, sig.=0.000<0.05, therefore there is at least one independent variable
has the relationship with the dependent variable. Hence, the combination of independent
can explain the change of the dependent variable (CRM performance). This means the
build research model in this study conforms to collected data.
Model summary is shown in Table 12. R2=0.396 so relationship between
Customization, Employee-Client relationship and CRM performance is medium (0<R2<
0.5) or 1-R2 = 0.604 is explained by variables not appeared in model. This is one of
limitations of study.
Table 12. Model Summary
R
.629
R Square Adjusted R Square
.396
Std. Error of the Estimate
Durbin-Watson
.571
.014
.391
4.4.3 Testing hypotheses of relationship between the independent variables and
the dependent variable
The Pearson correlation matrix obtained for the five interval-scaled variables is
shown in Table Correlations (Appendix B). From the results, it can be seen that the
CRM performance, as would be expected, significantly positively correlated to
Customization, and Employee-Client relationship. It is important to note that no
60
correlation exceeded 0.5 for this sample. If correlations between the dependent variables
were higher (0.70 and above), collinearity problem might have been had in regression
analysis.
As mentioned in chapter three, multicollinearity phenomenon is also detected in
multiple regression analysis. Table 13 shows that three independent variables
(Customization, Employee-Client relationship, and Communication) in model have
significant meaning (sig.=0.000<0.05) and have the variance inflation (VIF) which are
smaller than 10. Therefore, there is no multicollinearity problem in studied data.
Table 13. Coefficients
Unstandardized Standardized
Coefficients
Coefficients
Beta
Collinearity
Statistics
B
Std. Error
t
Sig. Tolerance VIF
(Constant)
1.207
.149
Customization
.307
.046
.340
6.733 .000
.497
.343
Employee-Client
Relationship
.245
.032
.342
7.622 .000
.468
.382
Two way
communication
.108
.038
.148
2.818 .005
.452
.151
8.105 .000
Table 12 also represents three independent variables (Customization, EmployeeClient relationship and Two way communication) have positive standardized
coefficients Beta and those significance are all smaller than 0.05. The independent
variable ‘Collaborative Technology’ is not included in the regression model. Hence, in
this study, within sample of e-banking services customers in Danang, respondents didn’t
perceive much about Collaborative Technology offered by their banks.
Regression equation:
YCRMperfomance(i) = 1.207 + 0.307*Cus + 0.245*Rel + 0.108*Com + ε(i)
These numbers mean that if perception of e-banking customers about Customization,
Employee-Client relationship, and Two way communication are improved, CRM
performance would be increased (see Figure 15).
61
Cus2 0.869
Cus3 0.754
Customization
Cus1 0.600
0.340***
Rel3 0.889
Rel2 0.868
Rel1 0.685
Employee-Client
relationship
connection
Two way
communication
Tech4 0.871
Tech3 0.819
CRM
performance
R2 = 0.396
0.148**
Com3 0.822
Com1 0.718
0.342***
n.s
Collaborative
technology
Tech2 0.732
n.s = not significant
*
= p < 0.10
**
= p < 0.05
***
= p < 0.01
Figure 15. Empirical model
Source: Built on analysed data
Hypothesis 1: Collaborative technology has positive relationship with CRM
performance of electronic banking services
The regression analysis result did not support the hypothesis 1 – Collaborative
technology has positive relationship with CRM performance of electronic banking
services. This result is different from other researches (Jayachandran et al., 2005;
Becker et al., 2009) but has the same result with Reinartz et al. (2004).
According to Jayachandran et al. (2005), customer relationship performance is
enhanced by relational information processes whether CRM technology use is low of
high. However, as relational information processes go from low to high, customer
62
relationship performance improves more rapidly for a high level of CRM technology
use than for a low level of CRM technology use. It means that CRM technology use
boosts customer relationship performance in conjunction with relational information
processes. The writers also said that when appropriate relational information processes
were not implemented, the use of CRM technology might did more harmful. Likewise,
Becker et al. (2009) also found that the performance of technological implementations is
moderated by its users’ support.
Reinartz et al. (2004) in research of CRM’s measurement and impact on
performance hypothesized that CRM technology has a positive, moderating effect on
CRM processes-economic performance link at each stage of the relationship. This
hypothesis was only supported in termination stage (not supported in maintenance stage
and negative relationship in initiation stage). The authors also concluded that the
sophistication of the CRM technology use is not necessarily linked to a company’s
ability to improve performance through CRM processes.
Perhaps in Vietnam, CRM processes of commercial banks are in initiation stage
and/or maintenance stage, not termination stage and there is very little user support
activity. Furthermore, collaborative CRM technology is also called communicational
CRM technology. In e-banking sector, it can be reflected in communication through
various media such as call center, mobile banking, e-mail, internet banking… This issue
is expressed in the item Com1 (inform important information via various media
regularly) and Com3 (inform certain services will be available or not) from the variable
‘Two way communication’.
Hypothesis 2: Two way Communication has positive relationship with CRM
performance of electronic banking services
This hypothesis is supported. In comparison with the other ones (‘Customization’
and ‘Employee-Client relationship’), ‘Two way communication’ has least effect on
CRM performance (with coefficient = 0.148). This dimension included item Com1Inform via various media and item Com3-Inform services are available or not. Maybe
these offerings in electronic banking sector have become popular and compulsory so
that not much affect to CRM performance.
63
Hypothesis 3: Customization has positive relationship with CRM performance of
electronic banking services
Regression result supported the hypothesis 3. It showed that the variable
‘Customization’ has positive influence on CRM performance with coefficient = 0.340
and strong significance (p<0.01). Hence, for electronic banking sector in Vietnam,
‘Customization’ is also one of the most important factor which affects CRM
performance. The customization in this area is conducted through customization of
services, communication based on desired way, individualization of marketing messages.
Hypothesis 4: Employee-Client relationship has positive relationship with CRM
performance of electronic banking services
The regression analysis supported the hypothesis 2. ‘Employee-Client relationship’
has positive relationship with CRM performance (has strongest effect on CRM
performance with coefficient = 0.342). This is the distinctive point which belongs to
Vietnam culture. It can be seen in Vietnam, establishing and maintaining relationship
between customers and their banks are based on their particular acquaintance with
employees (like relatives or close friends).
4.5 Result of internal evaluation of two banks
4.5.1 Evaluating four elements by employees of two banks
Besides testing four elements from research model, a questionnaire to check level of
these factors was designed. The respondents are employees from two commercial
banks – Vietcombank and BIDV (31 questionnaires for each bank) (See Appendix B).
Ten levels of every aspects were adopted from scale of Lindgreen et al. (2005). Table 14
shows results of this.
Table 14. Internal evaluation of Vietcombank and BIDV
Vietcombank
BIDV
Highest level
Customer-interaction
strategy
6.23
5.58
10.00
Customization
7.26
6.42
10.00
64
Culture
7.03
7.55
10.00
Information
technology
6.81
6.23
10.00
As shown in Figure 16, except ‘Culture’ aspect of BIDV is greater than this average
number of Vietcombank, three rest elements of BIDV is all lower. This result helps us
explain why ‘Customization’ and ‘Customer-interaction strategy’ has stronger effect to
CRM performance than ‘Culture’. After factor analysis as presented above, ‘Customerinteraction strategy’ (equivalent
Communication in
e-banking services) and
Customization have become one component with name ‘Customization’.
Information
technology
Customerinteraction
strategy
10,00
8,00
6,00
4,00
2,00
0,00
Vietcombank
Customization
BIDV
Highest level
Culture
Figure 16. Internal evaluation by employees of Vietcombank and BIDV
4.5.2 Comparison with evaluating result by customers
To compare internal evaluation with external evaluation by customers, we can
consider ‘Two way communication’ (Com1, Com3), ‘Customization’ (Cus1, Cus2,
Cus3), and ‘Employee-Client relationship’ (Rel1, Rel2, Rel3). Table 15 shows the
results of perception of customers toward these four factors.
Table 15. External evaluation of Vietcombank and BIDV
Vietcombank
BIDV
Highest level
Two way
communication
3.39
3.38
5
Customization
3.50
3.69
5
Employee-Client
relationship
2.30
2.65
5
Technolog
y
2.81
2.43
5
As can be seen clearer in Figure 17, ‘Technology’ in customers’ perception is not as
high as evaluation of employees. In spite of this, these two evaluations are rather
65
similarly. So we can apply the scale of 0-10 levels to evaluate operational CRM for
other banks.
Interaction
5.00
4.00
3.00
2.00
Vietcombank
1.00
Technology
Customization
0.00
BIDV
Highest level
Connection
Figure 17. External evaluation by customers of Vietcombank and BIDV
66
Chapter 5 . CONCLUSIONS AND IMPLICATIONS
This section will summarize conclusions about research questions and suggest some
implications for management in commercial banks. In addition to these implications,
this chapter will conclude limitations of study and suggestions for future research.
5.1 Summary of study
The study aims to examine the influence of independent variables of operational
CRM on CRM performance in viewpoint of Vietnam e-banking customers. A research
model was built at chapter two consisting of assumed independent variables:
Collaborative CRM Technology, Two-way communication, Customization, and
Employee-Client relationship. Meanwhile, CRM performance was considered as the
dependent variable.
Of the 400 questionnaires sent out, 356 were received, a 89% response rate. There
are 344 questionnaires which were input to SPSS for analysis. Participants were nearly
equal in gender (50.6% of men and 49.4% of women). Most of them were young (at the
age of 18-35). Approximately 2.9% of the participants did not complete high school
education, whereas 88.9% had at least a bachelor’s degree.
Testing goodness of measure was conducted. After that, factor analysis was used.
From the 18 items (after finishing pilot test) of four independent variables and one
dependent variable, three of them took out from the scale (Tech1 – Extent of using
banking kiosk, Com2 – Be invited to take part in client meeting, Perf5 – Customer
commit to bank completely). The remaining items are loaded significantly on four
factors as presented in Table 16. Both Explanatory factor analysis and Confirmatory
factor analysis also supported this result.
Table 16. Remaining loading items
Independent variables
Dependent variable
Customization
Employee-Client
relationship
Two way
communication
Collaborative
Technology
CRM performance
Cus2
Cus3
Cus1
Rel3
Rel2
Rel1
Com3
Com1
Tech4
Tech3
Tech2
Perf1
Perf2
Perf3
67
Perf4
The multiple regression analysis result shows that Customization, Employee-Client
relationship, and Two way communication have positive effects on CRM performance
(0.340, 0.342 and 0.148 respectively). Although Collaborative Technology variable is
expected to influence positively CRM performance, it does not have significance in the
model. This relationship can be also expressed in an equation: YCRMperfomance(i) = 1.207 +
0.307*Cus + 0.245*Rel + 0.108*Com + ε(i)
R2=0.396 means that the independent variables explain 39.6% of the variance in the
dependent variable CRM performance. In the other words, it can be said that 39.6% of a
possible change in the level of CRM performance is caused by Customization and
Employee-Client relationship. The low variance (R2) can be attributed to the fact that it
would be possibly some variables which have influences on CRM performance but they
are not mentioned in research model.
5.2 Implication
This study provides several academic and managerial contributions. First of all,
elements, relating to operational CRM and relationship culture, which have been
expected affect CRM performance in the research model including both academic and
practical perspectives. The study not only mentions a new approach in researching CRM
performance but also suggests some implications for management in banks.
5.2.1 Theoretical Implications
The purpose of the study is to investigate the determinants of CRM performance in
organizations which have applied CRM with different levels and different rates of
success. There are several studies about enhancing CRM implementation in commercial
banks or impact of CRM components on customer loyalty. But it still lacks of studies
about perception of customers at operational CRM, especially in electronic environment.
Hence, this study is conducted to look insight into this issue.
To build a research model determining which factors influence CRM performance,
previous studies relating to CRM performance and variables belonging operational
CRM had been reviewed. There are many points of view when researching CRM, but in
68
general, CRM implementation often exists as three levels: analytical CRM, operational
CRM, and strategic CRM. Among them, analytical CRM tends to how firms understand
their customers (their ability in identifying and differentiating customers) so it is
difficult for customers to evaluate exactly. Meanwhile, operational CRM tends to the
way firms treat customers individually. Hence, this is a proper aspect for customers’
perception. In addition, the element belonging to stragic CRM that customer can easily
perceive is culture. This is customer-centric culture which expressed through
relationship between employees and customers.
For the reasons mentioned above, in order to research CRM performance in
customers’ view, the keywords such as CRM, performance, customers’ view,
operational CRM, culture, electronic banking were used to seek for the appropriate
literature. The articles relating to determinants of CRM performance, CRM output were
collected and their titles, abstracts were reviewed. Then, the research was extended to
banks and financial firms.
Before determining the research variables, in-depth interviews with the experts were
conducted to distinguish different factors which had the same concept but only named
differently. Based on literature review, three dimensions were entered in research model
including Collaborative CRM Technology, Two-way communication, Customization. In
addition, Employee-client relationship was considered as Culture element. These four
dimensions were regarded as independent variables which had influences on dependent
variable – CRM performance. Then, a questionnaire was designed, pretested and
distributed to e-banking customers of local commercial banks in Danang city. After
gathering all the data, reliability of scale and exploratory factor analysis were tested.
Then, hypotheses were tested by using multiple regression analysis as shown in the
summary.
One of the most important implications of the study is considering customers’ view
about CRM. Besides this, the study has developed a dimension relating to Culture
aspect (Employee-Client relationship). This is an issue which had not been mentioned in
studies about CRM performance. The result of data analysis also supported relationship
between this variable and CRM performance.
69
In addition to external evaluation by customers to define which determinants have
effects on CRM performance, a questionnaire based on assessment tool from Lindgreen
et al. (2005) was applied to test levels of deploying operational CRM and culture in two
commercial banks. This internal evaluation is similar to perception of customer.
Testing hypotheses showed that Customization (Customized interaction), EmployeeClient relationship, and Two way communication have direct effect on CRM
performance. Meanwhile, hypothesis 1 (positive relationship between Collaborative
CRM Technology and CRM performance) was not supported as expected. It is possible
to observe that Customization mostly affects CRM performance. Only three dimensions
explained 39.6% variance of CRM performance. This number is a rather high value in
behavioral science.
5.2.2 Managerial Implications
Outcomes of this study leads to some managerial implications that organizations
should pay attention to CRM deployment and want to enhance CRM performance.
At first, the outcomes of this study may be applicable to any other industry
(especially financial firms offering electronic services). Secondly, although e-banking
sector is an area which is difficult to customize, banks can offer customizations through
customized interactions. By this way, customers can perceive that they are treated in
individually. But not all the customers are interested in specific relationships (Keremati
et al., 2010), so firms should note that do not try to apply the same relationships as the
other ones do. As the result of this, every customer should be interacted through his/her
interesting channel. Permission marketing can be applied in this case. Managers can
refer ten e-mailing tips suggested by Vlam (2003):
1. Stop sending general information
2. Use sophisticated, clean databases. Don’t use a blunderbuss approach. Make
sure you have opt-in addresses: people who don’t mind being approached with
commercial messages.
3. Produce concise, tailored reports.
4. Approach your potential customer intelligently; don’t push your product too
hard. The customer is no fool.
70
5. Try starting a one-on-one dialogue with the customer.
6. Create reports. Keep track of how often you have approached a customer, in
which stage of contact you are at the moment; don’t send the same e-mail twice.
7. Notify the recipient. Provide information on the reasons why he or she has
received the e-mail.
8. State the sender’s name on the e-mail.
9. Indicate clearly what the recipient must do in order to stop future e-mails.
10. Always send to personal e-mail address. Never send to [email protected], [email protected],
etc.
The call center is the place where the message traffic is managed and handled as it
passes through different channels. In an efficient yet effective manner, attempts are
made to conduct dialogues with prospects and customers via telephone, fax and the
internet. An optimal balance is sought between technology and manpower. On the one
hand, technology will have to ensure efficiency, convenience, reliability, and
information on products and customers; on the other hand, it will contribute to a
reduction in costs and an increase in productivity. However, and this is certainly the
case with a CRM strategy, people will remain indispensable for lending a ‘human
touch’ to the contact. At crucial moments, they will have to ‘show their faces’, allowing
themselves to be supported by technology.
The quality of the call center depends to a great degree on the organization’s
accessibility. Professional capacity management must ensure that there are enough
personnel available at the various times for the completion of the communication. In
addition, the quality is determined by the quality of the contact. Telephone contact
involves questions such as: are the agents capable of listening and do they have the
desired communication skills and knowledge to provide customers with solutions? Are
they sufficiently well-equipped to make good on their promises and can they rely on the
back office? Have they had enough education and training? Is there room to learn and
make improvements and be able to focus on the customer’s wishes? In e-mail traffic, the
quality is primarily dependent on the respect for the customer’s privacy. High quality
71
may be offered if customization is supplied in the e-mails and answers are provided
promptly to customers’ questions (Peelen, 2005).
Thirdly, Employee-Client relationship has positive influence on CRM performance.
Hence, banks should not cancel face-to-face encounters toward e-banking customers.
These encounters do not need to be on a regular basis but punctual, at right places. This
task is often pertaining to front-office with customer-centric culture.
Peelen et al. (2000) also mentioned the issue of channel. This is similar to various
media mentioned in Com1 – Bank informs new or important banking information
through various media regularly. Hence, banks need to know types of e-banking
customers to interact with them via proper channel. Basically, consumers were divided
into eight types (Peelen et al., 2005) (see Table 17):
Table 17. Types of consumers
Passive
Active
Types
Conservative
Social
1
Instrumental
2
Social
3
Instrumental
4
Source: Built from research of Peelen et al. (2005)
Open
5
6
7
8
Conservative consumers are focused on security and familiar forms and frameworks.
They view new media somewhat skeptically. Open consumers are the opposite of
conservative consumers and are eager to experiment with new things, such as new
media. They have more knowledge of media, relatively speaking.
Some of conservative consumers are passive and have a strong tendency to focus on
judgment cast upon them by others or to attach a great deal of importance to these
opinions. They are more likely to trust others and very relationship oriented (Peelen et
al., 2005). E-banking customers of this type should be approached via appropriate
channel to maintain and develop relationship. Others, however, are active and have a
need for control, prefer to follow their own lead and ignore ‘true’ advice from the others.
Customers may be instrumentally or socially oriented. Instrumental people are more
efficient, purposeful and more individually oriented. They rely on ‘objective aspects’
than ‘good feeling’. So banks can focus on element ‘customized interaction’ than
72
‘Employee-Client relationship’ such as direct mail toward this type. Socially oriented
customers just find contact with others pleasant, they prefer face-to-face contact to the
telephone, and most certainly prefer it to the internet (see more in table 18). Efficiency
is not the highest goal, it is considered important to get a ‘good feeling’ from the things
that are done. Type 1 customer is the least open to new channels and is not after control,
type 8 is the opposite in this regard.
Table 18. Use of media by various customer types
Phase
Observation
Telephone
Internet
Direct mail
Billboard
8,6,7 (2-5)
2,4,6,8
1-8
General
information
8,6,7/1,2,3
8,6,7 (2-5)
Targeted
information
processing
1-8
8,6,7 (2-5)
Choice of
supplier or
brained
8,6,7/1,2,3
Transaction
8,6,7/1,2,3
8,6,7
2,4,6,8
After-sales
period
8,6,7/1,2,3
1-8
Source: Extracted from research of Peelen et al. (2000)
The fourth issue is to avoid the risk of losing relationship with customers when
employees move to another competitor, banks should develop relationship between
customers and brand of bank instead of private relationship between employees and
clients. Relationships take time to develop and must be nurtured, but once they develop,
customers feel a genuine, long-lasting sense of loyalty to the enterprise or brand. Most
customers want to deal with enterprises and use brands they feel they can trust and rely
upon— organizations with which they feel comfortable, those that treat them fairly and
honestly (Barnes, 2001). It means that employees must stand for their banks, not private
individual. To guarantee this, banks must have customers’ database updated regularly.
This database is concerned with demography, attitude, priority interaction channel,
behavioral data of customers. Norms regarding customer value such as transaction
frequency, transaction value, monthly income… should be collected to have appropriate
customer strategies.
Lastly, from comparison between internal and external evaluations, the results are
analogous. Hence, in order to enhance CRM performance through increasing
Customization and Employee-Client relationship, banks can use internal assessment tool
73
periodically. Assessment tool of Lindgreen et al. (2005) is one of tools which can be
referred (see Appendix B).
5.3 Research limitations and suggestion for future research
Firstly, because of limited time and cost, the study was only conducted in Danang
with convenience sampling. This can lead to restriction in demography of respondents.
This is also the least reliable form of non-probability sampling. The future research can
concentrate on some commercial banks which have big market share. Hence, probability
sampling can be used. In addition, we can choose different types of banks to make
comparisons.
Secondly, since e-commerce and e-banking are young fields in Vietnam, the
perception of customers about e-banking CRM performance is restricted. If this
knowledge enhanced (almost customers use different facilities of e-banking instead of
using credit cards as means of store), future researches could be more accurate.
Because R square is equal to 0.396, some variables may not be entered in the model
yet. In future researches, variables such as Customer loyalty programs, User support,
Type of customers… can be added as independent variables.
5.4 Final conclusions
This research identifies the variables influencing CRM performance in e-banking
sector in customers’ view and how strong these relationships are. It is proposed that this
research, focusing on CRM and electronic banking services, will be beneficial for
Vietnamese banks, especially in Danang city. The results may be used to ensure higher
levels of CRM and enhance the understanding of relationship between banks and their
e-banking customers. By this way, banks can reduce cost based on increasing offers
through electronic channels so this study in some way contributes to ensure the success
of banks.
The findings of this research create a greater awareness among local commercial
banks of the advantages of CRM performance, the variables influencing it, and what
they can adapt to positively influence their CRM performance. This also leads to
benefits for banks, their clients and the Vietnam economy as whole.
74
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APPENDIX
APPENDIX A. QUESTIONNAIRES
English Questionnaire
Dear sir/madam,
I am Trương Thị Vân Anh, working at Danang university of Economics. I am conducting
research on performance of Customer Relationship Management (CRM) in electronic banking
sector in Danang city. Please note that electronic banking service (e-banking) is any service
which processed through electronic channels such as auto teller machines (ATMs), point of sale
(POS), mobile banking, and internet banking…
The results of this will help commercial banks enhance electronic banking services in more
friendly way. All information provided will be treated in a confidential way and only used for
research purpose. Please spend few moments in completing the following questionnaire.
1. Everyone can make transactions with many banks. For this reason, please
choose only one bank which you often use their e-banking services to answer
these following questions.
The bank you have chosen is: ....................................................................................
2. Please indicate for which purposes you make use electronic banking services:
Personal
Business
3. How long have you been using electronic banking services:
Under 1 year
From 1-3 years
Both
Above 3 years
4. Please mark into square at every statement, use 1-5 scale with 1 = not at all,
2=very little, 3 = to some extent, 4=to a moderate extent, and 5 = strongly to
indicate the extent to which the following technologies have been used by you:
Electronic banking technologies
1 2 3 4 5
a. Banking kiosk (ATM/POS)
b. Online channel (via Mobile banking/Call Center/Website)
c. Email
d. Internet banking
Similarly to above evaluated way, from question 5 to question 8, please select with:
1
2
3
4
5
Strongly Disagree
Disagree
No comments
Agree
Strongly Agree
5. Your relationship between you and your bank depends on:
Statements
1
a. Your bank informs you new or important banking information
through various media regularly
b. Your bank often invite you take part in client meeting
80
2
3
4
5
c. Your bank informs you when certain services will be available
or not regularly
6. Your evaluation about customization supplied by your bank:
Statements
1
a. Your bank communicate with you based on the way you desire
b. Your bank customize electronic banking services to you
c. Your bank individualize marketing messages to you
2
3
4
7. How do you perceive about relationship between you and bank’s employees:
Statements
1 2 3 4
a. Your relationship based on particular acquaintance (such as
relatives/close friends…)
b. Employees visit you regularly
c. Employees visit you accompanied by the bank’s top managers
5
5
8. How do you perceive about performance of relationship between you and your
bank:
Statements
1 2 3 4 5
a. You have a high concern for the success of your bank
b. You know that your bank will always act in your best interest
c. You feel that you have a strong bond with your bank
d. You receive many benefits due to your relationship with your
bank
e. You commit to your bank completely
Except the bank you has chosen above, which bank(s) you also use their e-banking
services (you can named many banks): ..............................................................................
.............................................................................................................................................
9. The following information aims to statistics and will be in secure, please select
your information:
Age:
Under 18
18-35
36-45
46-60
Over 60
Sex:
Male
Female
Education level:
Primary
High school
Intermediate school/Colleges
University
Higher
Monthly income (million VND):
Lower 5
5-10
10-18
18-32
Over 32
11. Please fill in blanks if possible:
Full name: ...........................................................................................................................
Contact address: ..................................................................................................................
Email: ................................................. Telephone/cell phone: ........................................
If you are interested in this research, please contact me at [email protected] – cell phone: 0905173767
Thank you for your cooperation! Best wishes for you and your family!
81
Vietnamese Questionnaire
PHIẾU KHẢO SÁT
Xin chào anh/chị!
Tôi là Trương Thị Vân Anh, hiện đang công tác tại Trường Đại học Kinh tế, Đại học Đà
Nẵng. Tôi đang nghiên cứu về hiệu quả của Quản trị Quan hệ khách hàng (CRM) trong lĩnh vực
ngân hàng điện tử tại thành phố Đà Nẵng. Xin lưu ý khái niệm ngân hàng điện tử là bất cứ hoạt
động ngân hàng nào được thực hiện thông qua phương tiện điện tử như quầy giao dịch tự động
(ATM), điểm chấp nhận thẻ (POS), ngân hàng qua điện thoại, qua mạng internet…
Kết quả nghiên cứu sẽ góp phần giúp các ngân hàng hoàn thiện dịch vụ ngân hàng điện tử,
thân thiện và thuận tiện hơn cho người sử dụng. Thông tin mà anh/chị cung cấp sẽ được bảo mật
và chỉ sử dụng cho mục đích nghiên cứu. Rất mong anh/chị bớt chút thời gian để hoàn thành
phiếu khảo sát này.
1. Thực tế mỗi cá nhân có thể giao dịch với nhiều ngân hàng. Để thống nhất,
anh/chị hãy chỉ chọn một ngân hàng mà mình thường xuyên sử dụng dịch vụ
ngân hàng điện tử của ngân hàng này nhất để trả lời cho những câu hỏi tiếp theo.
Ngân hàng anh/chị đã lựa chọn là: ............................................................................
2. Anh/chị sử dụng dịch vụ ngân hàng điện tử vì mục đích nào:
Cá nhân
Công việc
Cả hai
3. Anh/chị đã sử dụng dịch vụ ngân hàng điện tử bao lâu:
Dưới 1 năm
Từ 1-3 năm
Trên 3 năm
4. Anh/chị hãy đánh dấu vào ô vuông ở mỗi phát biểu, dùng thang đo từ 1 đến 5
(với 1=không hề, 2= rất ít, 3=một chút, 4=vừa phải, 5=nhiều) để chỉ mức độ giao
dịch hoặc liên hệ của mình với ngân hàng thông qua các kênh sau:
Công nghệ ngân hàng điện tử được sử dụng
1 2 3 4 5
a. Kiosk ngân hàng (ATM/POS)
b. Hỗ trợ trực tuyến (qua điện thoại di động, Call Center, trang
web ngân hàng)
c. Thư điện tử (email)
d. Ngân hàng qua mạng internet
Tương tự cách đánh giá như trên, từ câu 5 đến câu 8, anh/chị chọn với:
1
2
3
4
5
Hoàn toàn không đồng ý Không đồng ý Không ý kiến Đồng ý Hoàn toàn đồng ý
5. Quan hệ giữa anh/chị và ngân hàng phụ thuộc vào việc:
Phát biểu
1
a. Ngân hàng thường xuyên thông báo tới anh/chị thông tin quan
trọng qua nhiều phương tiện khác nhau
b. Ngân hàng thường mời anh/chị tham dự hội nghị khách hàng
c. Ngân hàng thường xuyên thông báo cho anh/chị khi nào dịch vụ
sẵn sàng
82
2
3
4
5
6. Đánh giá của anh/chị về cách thức ngân hàng tiếp cận hoặc cung cấp dịch vụ
cho mình:
Phát biểu
1 2 3 4 5
a. Ngân hàng giao tiếp với anh/chị theo cách mà anh/chị muốn
b. Ngân hàng cung cấp dịch vụ ngân hàng điện tử theo nhu cầu
của anh/chị
c. Ngân hàng gửi thông tin về dịch vụ phù hợp với nhu cầu
anh/chị
7. Cảm nhận của anh/chị về quan hệ giữa mình với nhân viên ngân hàng:
Phát biểu
1 2 3 4 5
a. Anh/chị giao dịch với ngân hàng vì thân quen với nhân viên
ngân hàng
b. Nhân viên ngân hàng thường thăm hỏi anh/chị
c. Nhân viên ngân hàng cùng với cấp quản lý của mình thăm hỏi
anh/chị
8. Cảm nhận của anh/chị về sự gắn kết giữa mình với ngân hàng:
Phát biểu
1 2 3 4 5
a. Anh/chị rất quan tâm tới sự thành công của ngân hàng
b. Anh/chị biết rằng ngân hàng sẽ luôn vì lợi ích cao nhất của
anh/chị
c. Anh/chị cảm thấy có mối liên kết mạnh với ngân hàng
d. Anh/chị nhận được nhiều lợi ích từ quan hệ giữa mình với ngân
hàng
e. Anh/chị ủy thác hoàn toàn cho ngân hàng
Ngoài ngân hàng đã chọn, anh/chị còn sử dụng dịch vụ ngân hàng điện tử của ngân
hàng nào khác (có thể nêu tên nhiều ngân hàng): .............................................................
.............................................................................................................................................
9. Những thông tin dưới đây chỉ nhằm mục đích thống kê và sẽ được bảo mật,
anh/chị vui lòng lựa chọn thông tin phù hợp:
Tuổi:
Dưới 18
18-35
36-45
46-60
Trên 60
Giới tính:
Nam
Nữ
Trình độ:
Dưới THPT
THPT
THchuyên nghiệp/Cao đẳng
Đại học
Sau đại học
Thu nhập hàng tháng (triệu đồng):
Dưới 5
5-10
10-18
18-32
Trên 32
11. Nếu có thể, anh/chị vui lòng cho biết những thông tin sau:
Họ và tên:.............................................................................................................................
Địa chỉ liên lạc: ....................................................................................................................
Email: .................................................... Số điện thoại: .................................................
Nếu anh/chị quan tâm đến nghiên cứu, có thể liên lạc qua email [email protected] –hoặc số điện
thoại 0905173767
Chân thành cám ơn sự hợp tác của anh/chị! Kính chúc anh/chị và gia đình sức khỏe!
83
Operational CRM assessment tool
Source: Lindgreen et al. (2005)
Level
0
1
2
3
4
5
6
7
8
9
10
Level
0
1
2
3
4
5
6
Element 1: Customer-interaction strategy
We provide contact details so our customers can ask for information. We interact only rarely
with our customers, and this interaction is not coordinated between the different levels and
Functional departments in our organization.
We make an inventory of existing customer-touch points. We map these touch points for
different processes including information/communication, transaction, distribution, and service.
The characteristics of each touch point are described.
We analyze and understand customer-touch points in terms of their differences, functionalities,
importance, costs, and the business processes behind.
We define a customer-interaction strategy, which is aligned with our customer strategy. This
means that we serve customers through appropriate channels. Low-value customers are served
through low-cost channels, for example e-mail rather than face-to-face interaction.
We base the customer-interaction strategy primarily on our customers’ needs. Each interaction
with a customer has a clear objective, but we do not systematically capture a record of these
interactions using an information system.
We minimize our customers’ inconveniences by developing interaction channels. This helps us
to provide information, resolve problems and complaints, distribute goods and services, and
make transactions possible (e.g., order entry and online payment). However, these customized
interactions are still not well coordinated.
We have employees whose responsibility is to capture customer information provided by each
customer interaction. Every customer contact is recorded to get more insight into this
customer’s preferences and needs. We know when and how our customers want to interact with
us.
We track the effectiveness of our interaction channel(s), and use customer feedback for
improvements. Our employees in all functional areas know how best to respond quickly to a
customer request.
We coordinate and manage across all levels and functional departments in the organization each
single customer interaction. We achieve consistency in customer interactions.
We review continually our customer-interaction strategy. Interaction channels are used in an
effective and efficient way to avoid waste of resources.
We add value through our customer-interaction strategy. This influences our customers’
behavior so that they choose our organization. All channel opportunities are developed to create
channel synergy. Our customer-interaction strategy is translated into competitive advantages.
Element 2: Customization strategy
We sell our goods to customers who are willing to buy. We have no criteria in place to select
customers.
We have a customer strategy to select customers. Someone in our organization is responsible
for this strategy.
We define customer strategies, which are mainly focused on acquiring new customers.
We base our customer strategies primarily on the needs of prospective and existing customers,
rather than on (potential) customer-lifetime value.
We analyze the lifetime value of individual customers to understand their importance to our
organization. Different approaches including for example activity-based costing are used to
calculate the value of individual customers.
We rank customers by their value in order to define customer segments. Customers with similar
lifetime value are allocated to the same customer segment.
We set clear business objectives for each customer segment. We develop a corresponding value
proposition that is consistent with these objectives including, for example, a selling and pricing
strategy. In each segment customers have the same lifetime value, but are differentiated from
84
7
8
9
10
Level
0
1
2
3
4
5
6
7
8
each other by their needs.
We build and develop relationships with our most valuable customers. We continually analyze
their potential, and we take actions to transform unprofitable customers into profitable ones.
We retain our most valuable customers by understanding loyalty divers and by introducing
appropriate value-adding propositions. Moreover, we know why some customers defect and
how to win these customers back. We increase our customer retention by offering value-adding
propositions.
We meet the specific needs of our customers, and our value propositions regularly exceed their
expectations. We build unique relationships with our most valuable customers. Our customers
prefer our organization to do business with rather than our direct competitors because we excel
in creating value-adding opportunities. We review our customer strategy continually.
We develop excellent customer strategies, which create customer trust and commitment, and
drive the growth in our profitability. We are the number one strategic supplier of our most
valuable customers. In order to develop the most value-adding goods and services in the
marketplace we collaborate closely with our customers to exchange knowledge.
Element 3: Information Technology
We usually work with stand-alone systems, for example database marketing. There is no
structured way of working to collect and use customer data.
We set up separated information-technology systems in our organization to hold important
information about our customers such as transactions information. Some data is collected on
paper rather than in an information-technology system.
We determine which data is required to support customer management processes. Such data
includes historical data for customer transactions and customer contacts. This data is collected
within a particular business unit using several information technology systems. We understand
how technologies will support our business processes, and have defined system requirements.
We prioritize analytic needs of our organization before making major information-technology
investments.
We design and build a common data store such as a data warehouse or data mart. Data
fragmentation, however, still occurs.
We define in detail terms in databases to avoid differences in meanings by departments or user
groups. The visibility and accessibility of customer data (obtained from a variety of customertouch points) among customer-facing employees and other employees have been increased, but
integration of customer-contact channels is still not fully realized.
We avoid data fragmentation problems by consolidating all customer information collected
from various customer-contact channels: face-to-face such as sales representatives, fax, mail,
telephone, E-mail, and Websites to allow E-technology applications such as online billing, order
entry, and configuration. E-technology also makes it possible for our customers to validate or
refresh supply chain data or customer data. They can do this themselves, and more frequently
and accurately.
We integrate front- and back-office systems. Front-office applications such as portals pull
information from the back-office system such as enterprise resource planning systems. Data is
sourced from our customers_ legacy systems and external data sources.
We realize the integration of customer-contact channels. It allows the sharing and usage of
information about our customers, which support activities such as sales force automation,
customer contact, campaign management, customer-service management, and order and supplychain management. Before analysis, customer data must be cleaned (e.g., eliminating duplicated
or irrelevant data), grouped, and transformed into a consistent and usable format. Someone in
the organization is given the responsibility for the quality and the management of data within
the context of a single business function or process. The quality of the data is determined by the
following criteria: accuracy, consistency, reliability.
We develop insights into our customers by analyzing customer and market data extracted from
our databases. Information-technology system tools allow our organization to analyze and look
for patterns in customer data. These information-technology system tools, for example data
85
9
10
Level
0
1
2
3
4
5
6
7
8
9
10
mining, are able to identify profitable
customers and their characteristics; predict customer-buying behavior (by purchase analysis,
interaction/channel analysis, customer-response analysis, and market analysis); evaluate
marketing-campaign effectiveness; provide opportunities for cross and up selling; estimate
customers_ potential; and reveal factors that cause customers to remain loyal to our
organization.
We use innovative technologies including, for example mobile devices, to update customer data
in real time to provide each system and channel with the most recent customer information.
This way of working reduces the time to market. Our selection of technologies is validated by a
customer-oriented process.
We achieve an integrated, cross-functional, multiple-channel (contact channel) view of our
customers. We achieve this by the integration of consistent customer data and applications. This
comprehensive customer intelligence allows us to manage each customer relationship efficiently
and effectively, and to grow our business. The integration of information systems is extended to
our key partners and suppliers.
Element 4: Culture
We request our sales people to focus on single sales rather than on customer retention. The
focus is on short-term sales targets.
We are paying more attention to goods and competitors than to customers. We lack an
understanding of our customers’ needs and wants.
We are aware of the necessity of a customer-focused mindset, as well as an organizational
change for building relationships with our most valuable customers.
We, employees or departments, especially sales people, act in a more customer-centric way.
There is hardly any internal resistance to organizational or cultural change.
We delegate clear responsibility and authority to leaders in our organization in order to realize a
customer-focused culture. We request our leaders to understand the market, and to show
determination. Their style and methods of managing in turn are encouraging a customer
orientation, as well as our employees’ service mindedness.
We focus primarily on customers and long-term relationships rather than on goods and shortterm transactions. We react quickly to customer requests and demands.
We adapt the way of working in our organization: we now anticipate rather than react to our
customers’ requests and demands.
We constantly try to meet customers’ expectations by delivering appropriate goods and services
and by solving their problems quickly. Our employees are competent to communicate in a
customer-oriented way, and possess the required interaction skills.
We focus on creating value-adding opportunities for our customers. Our employees are
committed and dedicated to satisfying our customers. Employees feel responsible for the end
result and act with the customer in mind.
We constantly think from the customer’s point of view in order to improve business
performance. We emphasize on seeking new, innovative ways of working to serve our
customers individually. Also, we continuously try to exceed customers’ expectations and
requirements.
We instill a customer-focused culture in our organization. Customer focus and commitment are
parts of our corporate vision and mission. Honesty and openness characterize the way of
working. We involve in an early stage our customers and suppliers in product and service
development, and continue to monitor external developments.
86
Vietnamese Questionnaire for Internal evaluation
Kính chào anh/chị!
Tôi hiện đang công tác tại Khoa Thương mại, Trường Đại học Kinh tế Đà Nẵng. Tôi đang
nghiên cứu về hiệu quả của hoạt động Quản trị quan hệ khách hàng (CRM) tại các ngân hàng.
Phiếu này được xây dựng nhằm hỗ trợ anh/chị đánh giá mức độ triển khai CRM tại ngân hàng
mình, qua đó biết được cách thức để khách hàng cảm nhận tốt hơn về ngân hàng, gia tăng hiệu
quả của việc Quản trị quan hệ. Thông tin mà anh/chị cung cấp sẽ hoàn toàn được bảo mật và chỉ
phục vụ cho mục đích nghiên cứu. Rất mong anh/chị bớt chút thời gian để hoàn thành!
Anh/chị vui lòng khoanh tròn để chọn một con số bên cột trái (từ 0 đến 10) mà anh/chị cho là
phù hợp nhất thể hiện mức độ của từng yếu tố sau:
Mức
0
1
2
3
4
5
6
7
8
9
10
Mức
0
1
2
3
4
Yếu tố 1: Chiến lược tương tác khách hàng
Ngân hàng cung cấp chi tiết liên lạc để khách hàng có thể hỏi thông tin, tương tác với khách
hàng hạn chế, thiếu sự phối hợp các cấp giữa các bộ phận khác nhau trong ngân hàng
Ngân hàng tập hợp, mô tả đặc trưng của các điểm liên lạc với khách hàng hiện tại, hệ thống theo
những quy trình bao gồm thông tin/truyền thông, giao dịch, phân phối, và dịch vụ.
Ngân hàng phân tích và hiểu sự khác biệt, chức năng, tầm quan trọng, chi phí, quy trình kinh
doanh gắn với các điểm liên lạc
Ngân hàng xác định chiến lược tương tác với khách hàng tương ứng với chiến lược khách hàng,
nghĩa là phục vụ khách hàng qua kênh phù hợp. Khách hàng giá trị thấp được phục vụ qua kênh
chi phíthấp (chẳng hạn email thay vìtiếp xúc trực tiếp).
Ngân hàng lập chiến lược tương tác với khách hàng cơ bản dựa trên nhu cầu khách hàng. Mỗi
tương tác với từng khách hàng có mục tiêu rõ ràng, nhưng chưa có hệ thống thông tin lưu lại
những tương tác này một cách có hệ thống.
Ngân hàng giảm thiểu những bất tiện của khách hàng nhờ phát triển các kênh tương tác, giúp
cung cấp thông tin, giải quyết vướng mắc và phàn nàn, phân phối dịch vụ và thực hiện giao
dịch. Tuy nhiên, các tương tác này chưa được phối hợp tốt.
Ngân hàng có nhân viên chuyên trách lưu lại thông tin khách hàng qua mỗi tương tác để hiểu
khách hàng ưa thích và có nhu cầu gì, họ muốn tương tác với ngân hàng khi nào và cách thức ra
sao.
Ngân hàng theo dõi hiệu quả, cải thiện các kênh tương tác nhờ phản hồi từ khách hàng. Nhân
viên ngân hàng ở mọi bộ phận chức năng biết cách thức tốt nhất để đáp lại yêu cầu của khách
hàng nhanh chóng.
Ngân hàng phối hợp và quản lý qua mọi cấp và các bộ phận chức năng ở mỗi tương tác, đạt
được sự nhất quán trong tương tác với khách hàng.
Ngân hàng không ngừng xem lại chiến lược tương tác. Các kênh tương tác được sử dụng hiệu
quả để tránh lãng phínguồn lực.
Ngân hàng gia tăng giá trị thông qua chiến lược tương tác, khiến khách hàng lựa chọn ngân hàng
anh/chị. Mọi cơ hội kênh được phát triển nhằm tạo ra sự đồng vận trong kênh. Chiến lược tương
tác với khách hàng đã trở thành lợi thế cạnh tranh.
Yếu tố 2: Chiến lược cá biệt khách hàng
Ngân hàng cung ứng dịch vụ đến những khách hàng muốn mua, không có tiêu chuẩn lựa chọn
khách hàng.
Ngân hàng có chiến lược lựa chọn khách hàng với người chịu trách nhiệm riêng.
Ngân hàng xác định chiến lượng khách hàng chủ yếu tập trung vào việc đạt được khách hàng
mới.
Ngân hàng bố trí chiến lược khách hàng chủ yếu dựa trên nhu cầu của khách hàng hiện tại và
tương lai hơn là dựa trên giá trị lâu dài (tiềm năng) của khách hàng.
Ngân hàng phân tích giá trị lâu dài của từng khách hàng để hiểu tầm quan trọng của họ đối với
ngân hàng.
87
5
6
7
8
9
10
Mức
0
1
2
3
4
5
6
7
8
Ngân hàng xếp hạng khách hàng dựa trên giá trị để xác định phân đoạn khách hàng. Những
khách hàng với giá trị lâu dài tương tự nhau được xếp cùng phân đoạn.
Ngân hàng lập mục tiêu kinh doanh rõ ràng cho mỗi phân đoạn khách hàng, phát triển tuyên bố
giá trị tương ứng với những mục tiêu này. Mỗi phân đoạn có giá trị lâu dài tương đồng nhưng
phân biệt với phân đoạn khác bởi nhu cầu.
Ngân hàng xây dựng và phát triển quan hệ với những khách hàng giá trị nhất, không ngừng
phân tích tiềm năng của họ và hành động để chuyển khách hàng không sinh lợi thành khách
hàng sinh lợi.
Ngân hàng duy trì những khách hàng có giá trị nhất bằng việc hiểu được những yếu tố mang lại
lòng trung thành và giới thiệu những tuyên bố gia tăng giá trị phù hợp. Hơn nữa, ngân hàng biết
tại sao khách hàng rời bỏ và biết cách giành lại những khách hàng này.
Ngân hàng đáp ứng nhu cầu riêng biệt của khách hàng, và những tuyên bố giá trị thường đáp
ứng được mong đợi của họ. Ngân hàng xây dựng quan hệ đặc biệt với những khách hàng giá trị
nhất. Khách hàng thích giao dịch với ngân hàng anh/chị hơn ngân hàng đối thủ bởi ngân hàng
anh/chị trội hơn trong việc tạo ra cơ hội gia tăng giá trị. Ngân hàng cũng không ngừng xem lại
chiến lược khách hàng.
Ngân hàng phát triển chiến lược khách hàng xuất sắc, tạo ra lòng tin và sự thỏa thuận của khách
hàng, tạo đà cho sự phát triển và khả năng sinh lợi. Ngân hàng là nhà cung cấp chiến lược số một
của những khách hàng có giá trị nhất. Ngân hàng cộng tác với khách hàng để trao đổi kiến thức.
Yếu tố 3: Công nghệ thông tin
Ngân hàng thường làm việc với hệ thống dữ liệu riêng lẻ, chưa có cách thức để thu thập và sử
dụng dữ liệu khách hàng.
Ngân hàng thiết lập hệ thống thông tin riêng rẽ để lưu giữ thông tin quan trọng về khách hàng
(như thông tin giao dịch). Một số dữ liệu được thu thập trên giấy nhiều hơn là sử dụng hệ thống
thông tin.
Ngân hàng quyết định dữ liệu nào cần để hỗ trợ quy trình quản lý khách hàng (như dữ liệu lịch
sử giao dịch, địa chỉ liên lạc của khách hàng). Dữ liệu này được thu thập nội trong một đơn vị
kinh doanh riêng, sử dụng một vài hệ thống công nghệ thông tin. Ngân hàng hiểu công nghệ hỗ
trợ cho tiến trình kinh doanh ra sao và xác định các yêu cầu hệ thống. Nhu cầu phân tích của
ngân hàng được ưu tiên trước khi thực hiện những đầu tư công nghệ thông tin trọng yếu.
Ngân hàng thiết kế và xây dựng một kho dữ liệu chung nhưng vẫn còn dữ liệu rời rạc.
Ngân hàng xác định những thuật ngữ chi tiết trong cơ sở dữ liệu để tránh sự khác biệt về ý nghĩa
ở từng bộ phận hay nhóm người sử dụng. Sự minh bạch và khả năng truy cập dữ liệu khách
hàng của các nhân viên được tăng cường, nhưng sự tích hợp các kênh tương tác với khách hàng
vẫn chưa đầy đủ.
Ngân hàng tránh vấn đề phân mảnh dữ liệu bằng cách hợp nhất mọi thông tin khách hàng được
thu thập từ những kênh tương tác khác nhau (trực tiếp qua bộ phận giao dịch, fax, thư từ, điện
thoại, thư điện tử và những trang web cho phép ứng dụng công nghệ điện tử như hóa đơn điện
tử, đặt hàng… Công nghệ điện tử cũng khiến khách hàng tự cập nhật dữ liệu thường xuyên và
chính xác hơn.
Ngân hàng tích hợp hệ thống trực tiếp và gián tiếp. Dữ liệu có thể xuất phát từ hệ thống dữ liệu
khách hàng của ngân hàng hoặc từ nguồn bên ngoài.
Ngân hàng tích hợp các kênh tương tác với khách hàng, cho phép chia sẻ và sử dụng thông tin
về khách hàng nhằm hỗ trợ hoạt động tự động hóa lực lượng bán, liên lạc khách hàng, quản trị
chiến dịch, quản trị dịch vụ khách hàng, đặt hàng, quản trị chuỗi cung ứng. Trước khi phân tích,
dữ liệu khách hàng phải được làm sạch, loại bỏ dữ liệu thừa, nhóm lại và chuyển sang định dạng
sử dụng được. Ngân hàng có nhân viên chuyên trách về chất lượng và quản trị dữ liệu trong
phạm vi bộ phận chức năng hay tiến trình đơn lẻ. Chất lượng dữ liệu được quyết định bởi các
tiêu chuẩn: chuẩn xác, nhất quán, đáng tin cậy, tiếp cận được và hoàn chỉnh.
Hệ thống công nghệ thông tin cho phép ngân hàng phân tích và tìm kiếm mẫu trong dữ liệu
khách hàng, giúp nhận diện khách hàng sinh lợi, dự đoán hành vi mua, đánh giá hiệu quả của
chiến dịch marketing, tạo ra cơ hội kinh doanh thêm, nhờ đó duy trì khách hàng trung thành cho
ngân hàng.
88
9
10
Mức
0
1
2
3
4
5
6
7
8
9
10
Ngân hàng sử dụng công nghệ cải tiến để cập nhật dữ liệu khách hàng, tiết kiệm thời gian. Việc
lựa chọn công nghệ cũng dựa trên định hướng khách hàng.
Ngân hàng đạt được sự tích hợp xuyên chức năng, tương tác đa kênh với khách hàng nhờ tích
hợp dữ liệu khách hàng nhất quán, giúp quản trị quan hệ khách hàng hiệu quả. Sự tích hợp hệ
thống thông tin được mở rộng cho cả đối tác và nhà cung cấp trọng yếu.
Yếu tố 4: Văn hóa khách hàng
Ngân hàng yêu cầu nhân viên tập trung vào mục tiêu ngắn hạn, vào doanh số hơn là duy trì
khách hàng.
Ngân hàng chú ý vào dịch vụ và đối thủ cạnh tranh hơn là khách hàng, ít hiểu về nhu cầu và
mong muốn của khách hàng.
Ngân hàng nhận thức được sự cần thiết của việc tập trung vào tư duy khách hàng, cũng như sự
thay đổi tổ chức nhằm xây dựng quan hệ với những khách hàng giá trị nhất.
Ngân hàng (đặc biệt là bộ phận sales) hành động theo cách thức tập trung vào khách hàng hơn.
Hầu như không có đối kháng nội bộ nhằm thay đổi về tổ chức hay văn hóa.
Ngân hàng ủy quyền trách nhiệm và quyền hạn rõ ràng cho lãnh đạo nhằm thực hiện văn hóa tập
trung vào khách hàng. Lãnh đạo được yêu cầu phải hiểu thị trường và thể hiện sự quyết đoán.
Phong cách lãnh đạo và cách thức quản trị ngược lại sẽ thúc đẩy định hướng khách hàng cũng
như tinh thần của nhân viên.
Ngân hàng tập trung chủ yếu vào khách hàng và quan hệ lâu dài hơn là dịch vụ và những giao
dịch ngắn hạn. Ngân hàng phản ứng nhanh chóng với yêu cầu và mong muốn của khách hàng.
Ngân hàng lường trước hơn là phản ứng lại yêu cầu và mong muốn của khách hàng.
Ngân hàng luôn cố gắng đáp ứng mong đợi của khách hàng bằng việc cung ứng những dịch vụ
phù hợp và giải quyết vấn đề của khách hàng nhanh chóng. Nhân viên ngân hàng thành thạo
truyền thông theo cách thức định hướng khách hàng và sử hữu các kĩ năng tương tác cần thiết.
Ngân hàng tập trung vào việc tạo cơ hội gia tăng giá trị cho khách hàng. Nhân viên ngân hàng
đã cam kết và tận tâm nhằm thỏa mãn khách hàng. Nhân viên cảm nhận được trách nhiệm về kết
quả cuối cùng và hành động với tư duy về khách hàng.
Ngân hàng luôn nghĩ từ quan điểm của khách hàng để cải thiện hiệu quả kinh doanh. Ngân hàng
chú trọng việc tìm kiếm cách thức làm việc mới, cải tiến để phục vụ khách hàng một cách riêng
biệt. Ngân hàng cũng luôn cố gắng đáp ứng mong đợi và yêu cầu của khách hàng.
Ngân hàng thấm nhuần văn hóa tập trung vào khách hàng trong tổ chức của mình. Tập trung vào
khách hàng và cam kết là một phần trong viễn cảnh và sứ mệnh của ngân hàng.
Nếu anh/chị quan tâm kết quả nghiên cứu, có thể liên hệ qua email [email protected]
Chân thành cám ơn sự hợp tác của anh/chị. Kính chúc anh/chị và gia đình sức khỏe!
89
APPENDIX B. DATA ANALYSIS RESULT
Customer’s banks
Responses
N
Customer's bank
namea
Percent
Percent of Cases
Agribank
87
14.1%
25.3%
Vietcombank
89
14.5%
25.9%
Techcombank
22
3.6%
6.4%
ACB
16
2.6%
4.7%
200
32.5%
58.1%
Vietinbank
66
10.7%
19.2%
BIDV
61
9.9%
17.7%
Sacombank
20
3.3%
5.8%
54
615
8.8%
100.0%
15.7%
178.8%
Dong A bank
Others
Total
a. Group
Correlation Matrix of independent variables for the first EFA
Tech2 Tech3 Tech4 Com1 Com2 Com3 Cus1 Cus2 Cus3 Rel1 Rel2 Rel3
C Tech2 1.000 .503 .504 .189
or Tech3 .503 1.000 .600 .179
rel
ati Tech4 .504 .600 1.000 .148
on Com1 .189 .179 .148 1.000
.233
.284 -.032 .047 .128 .281 .118 .187
.311
.282 -.066 .076 .176 .332 .157 .242
.244
.188 -.125 .079 .126 .238 -.005 .125
.341
.459 .369 .374 .388 .199 .271 .239
.488 .112 .185 .315 .332 .360 .410
Com2
.233
.311
.244 .341 1.000
Com3
.284
.282
.188 .459 .488 1.000 .280 .337 .495 .215 .265 .275
Cus1
-.032 -.066 -.125 .369 .112
.280 1.000 .474 .356 .066 .169 .169
Cus2
.047
.076
.079 .374 .185
.337 .474 1.000 .521 .027 .127 .121
Cus3
.128
.176
.126 .388 .315
.495 .356 .521 1.000 .138 .259 .242
Rel1
.281
.332
.238 .199 .332
.215 .066 .027 .138 1.000 .410 .492
Rel2
.118
.157 -.005 .271 .360
.265 .169 .127 .259 .410 1.000 .750
Rel3
.187
.242
.275 .169 .121 .242 .492 .750 1.000
.125 .239 .410
90
KMO and Barlett’s test of independent variables for the first EFA
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of
Approx. Chi-Square
Sphericity
Df
Sig.
.795
1398.502
66
.000
Anti-image Matrices of independent variables for the first EFA
Tech2 Tech3 Tech4 Com1 Com2 Com3 Cus1 Cus2 Cus3 Rel1
Anti
ima
ge
Cov
aria
nce
Anti
ima
ge
Corr
elati
on
Rel2
Rel3
Tech2
.650
-.135
-.169
-.032
.019
-.089
.008
.027
Tech3
-.135
.542
-.232
.004
-.038
-.044
.042
.000 -.019 -.074 -.009 -.014
Tech4
-.169
-.232
.547
-.037
-.046
.028
Com1
-.032
.004
-.037
.665
-.066
Com2
.019
-.038
-.046
-.066
.638
Com3
-.089
-.044
.028
-.126
-.182
Cus1
.008
.042
.089
-.137
.045
Cus2
.027
.000
-.048
-.080
-.002
-.020 -.209
Cus3
.019
-.019
-.019
-.039
-.027
-.156 -.053 -.210
Rel1
-.061
-.074
-.031
-.028
-.061
Rel2
-.003
-.009
.083
-.057
-.027
Rel3
-.004
-.014
-.030
.027
-.068
Tech2
.831
a
-.228
-.283
-.048
.030
-.148
.012
.043
Tech3
-.228
.798a
-.427
.006
-.065
-.079
.069
.000 -.034 -.122 -.019 -.032
Tech4
-.283
-.427
.711a
-.061
-.077
.050
-.061
a
-.101
-.101
a
-.303
-.303
a
Com1
Com2
Com3
Cus1
Cus2
-.048
.030
-.148
.012
.043
.006
-.065
-.079
.069
.000
-.077
.050
.147
-.082
.881
-.206
-.205
-.125
.881
.068
-.003
Cus3
.030
-.034
-.033
-.062
-.044
Rel1
-.092
-.122
-.050
-.041
-.093
Rel2
-.006
-.019
.176
-.109
-.053
Rel3
-.008
-.032
-.067
.054
-.139
.019 -.061 -.003 -.004
.089 -.048 -.019 -.031
.083 -.030
-.126 -.137 -.080 -.039 -.028 -.057
-.182
.045 -.002 -.027 -.061 -.027 -.068
.562 -.048 -.020 -.156
-.048
.009 -.002
.672 -.209 -.053 -.024
.009 -.024
-.002
.016
.001 -.043
.610 -.210
.048
.048
.001
.016 -.043
.007
.005
.589
.011 -.039 -.003
.011
.680 -.044 -.120
.007 -.039 -.044
.407 -.258
.005 -.003 -.120 -.258
.378
.030 -.092 -.006 -.008
.147 -.082 -.033 -.050
.176 -.067
-.206 -.205 -.125 -.062 -.041 -.109
.838
.027
.054
.068 -.003 -.044 -.093 -.053 -.139
-.079 -.035 -.270
-.079 .765
a
-.327 -.084 -.035
-.035 -.327 .761
a
-.350
-.270 -.084 -.350 .834
.015 -.035
.015 -.004
.075
a
.075
.002
.030 -.086
.014
.010
.018 -.080 -.006
.018 .892a -.083 -.237
.030
.014 -.080 -.083 .700a -.658
.002 -.086
.010 -.006 -.237 -.658 .715a
-.004
Communalities of Independent variables for the first EFA
Initial
Tech2 (Online support channel)
1.000
91
Extraction
.593
Tech3 (Email)
Tech4 (Internet banking)
Com1 (Inform via various media)
Com2 (Be invited to client meeting)
Com3 (Inform services are available or not)
Cus1 (Individualize marketing messages)
Cus2 (Customize services)
Cus3 (Communicate based on desired way)
Rel1 (Connection)
Rel2 (Regularly visit)
Rel3 (Visit accompanied by managers)
Extraction Method: Principal Component Analysis.
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
.690
.718
.499
.462
.558
.560
.631
.593
.543
.788
.804
Total Variance Explained of Independent variables for the first EFA
Initial Eigenvalues
Extraction Sums of
Squared Loadings
Rotation Sums of
Squared Loadings
Compo
% of Cumula
% of Cumula
% of Cumula
nent Total Variance tive % Total Variance tive % Total Variance tive %
1
3.885 32.372 32.372 3.885 32.372 32.372 2.691
2
2.017 16.810 49.182 2.017 16.810 49.182 2.403
3
1.536 12.804 61.985 1.536 12.804 61.985 2.344
4
.794
6.617 68.602
5
.678
5.648 74.251
6
.581
4.843 79.094
7
.552
4.603 83.697
8
.519
4.324 88.021
9
.451
3.757 91.778
10
.391
3.255 95.033
11
.366
3.053 98.086
12
.230
1.914 100.000
Extraction Method: Principal Component Analysis.
22.429 22.429
20.027 42.455
19.530 61.985
Component Matrixa of Independent variables for the first EFA
Component
1
2
3
Com3 (Inform services are available or not)
92
.698
Com2 (Be invited to client meeting)
Rel3 (Visit accompanied by managers)
Com1 (Inform via various media)
Cus3 (Communicate based on desired way)
Rel1 (Connection)
Cus1 (Individualize marketing messages)
Tech4 (Internet banking)
Tech3 (Email)
Cus2 (Customize services)
Tech2 (Online support channel)
Rel2 (Regularly visit)
Extraction Method: Principal Component Analysis.
a. 3 components extracted.
.670
.654
.619
.617
.557
-.611
-.639
.605
.572
-.528
.526
.429
.546
.471
.486
.602
.409
-.648
Rotated Component Matrixa of independent variables for the first EFA
1
Cus2 (Customize services)
.793
Cus3 (Communicate based on desired way)
.747
Cus1 (Individualize marketing messages)
.703
Com1 (Inform via various media)
.655
Com3 (Inform services are available or not)
.636
Rel3 (Visit accompanied by managers)
Rel2 (Regularly visit)
Rel1 (Connection)
Com2 (Be invited to client meeting)
Tech4 (Internet banking)
Tech3 (Email)
Tech2 (Online support channel)
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 4 iterations.
Component
2
3
.883
.869
.671
.485
.846
.805
.756
Component Transformation Matrix of independent variables for the first EFA
Component
1
2
93
3
1
.634
.606
2
-.662
.105
3
.399
-.789
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
.481
.742
.468
Correlation Matrix of independent variables for the second EFA
Tech2 Tech3 Tech4 Com1 Com3 Cus1
Cus2
Cus3
Rel1
Rel2 Rel3
Correlati Tech2
on
Tech3
1.000
.503
.504
.189
.284 -.032
.047
.128
.281
.118 .187
.503
1.000
.600
.179
.282 -.066
.076
.176
.332
.157 .242
Tech4
.504
.600
1.000
.148
.188 -.125
.079
.126
.238 -.005 .125
Com1
.189
.179
.148 1.000
.459
.369
.374
.388
.199
.271 .239
Com3
.284
.282
.188
.459 1.000
.280
.337
.495
.215
.265 .275
Cus1
-.032
-.066
-.125
.369
.280 1.000
.474
.356
.066
.169 .169
Cus2
.047
.076
.079
.374
.337
.474 1.000
.521
.027
.127 .121
Cus3
.128
.176
.126
.388
.495
.356
.521 1.000
.138
.259 .242
Rel1
.281
.332
.238
.199
.215
.066
.027
.138 1.000
.410 .492
Rel2
.118
.157
-.005
.271
.265
.169
.127
.259
.410 1.000 .750
Rel3
.187
.242
.125
.239
.275
.169
.121
.242
.492
.750 1.000
KMO and Bartlett's Test of independent variables for the second EFA
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity
.768
Approx. Chi-Square
1247.707
df
55
Sig.
.000
Anti-image Matrices of independent variables for the second EFA
Tech2 Tech3 Tech4 Com1 Com3 Cus1 Cus2 Cus3 Rel1
Rel2
Rel3
AntiTech2
image
Tech3
Covarianc
Tech4
e
.650
-.135
-.168
-.030
-.092
.007
.027
-.135
.544
-.238
.000
-.060
.045
.000 -.021 -.079 -.011 -.019
-.168
-.238
.550
-.042
.016
Com1
-.030
.000
-.042
.672
Com3
-.092
-.060
.016
-.161
Cus1
.007
.045
.093
-.135
-.039
Cus2
.027
.000
-.048
-.081
-.023 -.210
94
.020 -.060 -.002 -.002
.093 -.048 -.021 -.036
.082 -.036
-.161 -.135 -.081 -.042 -.035 -.060
.021
.619 -.039 -.023 -.180 -.009 -.011 -.021
.675 -.210 -.051 -.020
.610 -.210
.048
.018 -.039
.007
.005
Cus3
.020
-.021
-.021
-.042
-.180 -.051 -.210
.590
.009 -.041 -.006
Rel1
-.060
-.079
-.036
-.035
-.009 -.020
.048
.009
.686 -.047 -.130
Rel2
-.002
-.011
.082
-.060
-.011
.007 -.041 -.047
Rel3
-.002
-.019
-.036
.021
a
-.226
-.282
-.226
a
-.434
AntiTech2
image
Tech3
Correlatio
Tech4
n
Com1
.824
-.282
-.045
.777
.000
.018
.408 -.267
-.021 -.039
.005 -.006 -.130 -.267
-.045
-.145
.010
.043
-.434
.000
-.104
.074
.000 -.037 -.128 -.023 -.041
a
-.069
.028
-.069
a
.694
.859
.032 -.090 -.005 -.003
.153 -.083 -.037 -.058
.173 -.078
-.250 -.200 -.126 -.067 -.051 -.115
a
.386
Com3
-.145
-.104
.028
-.250
.844
Cus1
.010
.074
.153
-.200
-.061 .769a -.328 -.081 -.029
.041
-.061 -.038 -.298 -.014 -.022 -.043
a
-.350
.075
.034 -.077
Cus2
.043
.000
-.083
-.126
-.038 -.328 .752
Cus3
.032
-.037
-.037
-.067
-.298 -.081 -.350 .811a
.014
.010
Rel1
-.090
-.128
-.058
-.051
-.014 -.029
.075
Rel2
-.005
-.023
.173
-.115
-.022
.034
.014 -.083 -.089 .666a -.673
Rel3
-.003
-.041
-.078
.041
-.043 -.077
.010 -.013 -.253 -.673 .682a
.014 -.083 -.013
.014 .875a -.089 -.253
a. Measures of Sampling Adequacy(MSA)
Communalities of independent variables for the second EFA
Initial
Tech2 (Online support channel)
Tech3 (Email)
Tech4 (Internet banking)
Com1 (Inform via various media)
Com3 (Inform services are available or not)
Cus1 (Individualize marketing messages)
Cus2 (Customize services)
Cus3 (Communicate based on desired way)
Rel1 (Connection)
Rel2 (Regularly visit)
Rel3 (Visit accompanied by managers)
Extraction Method: Principal Component Analysis.
Extraction
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
.615
.715
.761
.632
.770
.643
.787
.598
.585
.807
.825
Total Variance Explained of independent variables for the second EFA
Com
pone
Initial Eigenvalues
Extraction Sums of
Squared Loadings
95
Rotation Sums of Squared
Loadings
nt
% of
% of Cumula
Varian Cumulati
% of Cumulative
Total Variance tive % Total ce
ve % Total Variance
%
1
2
3
4
5
6
7
8
9
10
11
3.509
2.013
1.531
.684
.676
.562
.550
.467
.401
.375
.230
31.902 31.902 3.509 31.902 31.902 2.191 19.916
18.303 50.205 2.013 18.303 50.205 2.131 19.375
13.914 64.120 1.531 13.914 64.120 1.834 16.673
6.222 70.341 .684 6.222 70.341 1.581 14.377
6.148 76.490
5.112 81.601
4.999 86.601
4.245 90.846
3.650 94.496
3.410 97.906
2.094 100.000
Extraction Method: Principal Component Analysis.
19.916
39.292
55.965
70.341
Component Matrixa of independent variables for the second EFA
1
Com3 (Inform services are available or not)
Rel3 (Visit accompanied by managers)
Cus3 (Communicate based on desired way)
Com1 (Inform via various media)
Rel1 (Connection)
Cus1 (Individualize marketing messages)
Tech4 (Internet banking)
Tech3 (Email)
Tech2 (Online support channel)
Cus2 (Customize services)
Rel2 (Regularly visit)
Extraction Method: Principal Component Analysis.
a. 4 components extracted.
Component
2
3
.686
-.483
.648
-.629
.636
.631
.554
-.405
.411 -.624
.430 .620
.546 .589
.496 .544
.503 -.511
.600
-.665
Rotated Component Matrixa of independent variables for the second EFA
Component
96
4
1
2
Tech4 (Internet banking)
.871
Tech3 (Email)
.819
Tech2 (Online support channel)
.732
Rel3 (Visit accompanied by managers)
Rel2 (Regularly visit)
Rel1 (Connection)
Cus2 (Customize services)
Cus1 (Individualize marketing messages)
Cus3 (Communicate based on desired way)
Com3 (Inform services are available or not)
Com1 (Inform via various media)
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
3
4
.889
.868
.685
.869
.754
.600
.454
.822
.718
Component Transformation Matrix of independent variables for the second
EFA
Component
1
2
3
1
.465
.554
2
.744
.097
3
.432
-.818
4
.206
.117
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
4
.446
-.617
.311
.569
KMO and Bartlett's Test of dependent variable
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity
Approx. Chi-Square
Df
Sig.
Communalities of dependent variable
Initial
Perf1
Perf2
Perf3
.770
319.304
10
.000
Extraction
1.000
1.000
1.000
97
.526
-.237
.216
-.787
.427
.540
.587
Perf4
1.000
Perf5
1.000
Extraction Method: Principal Component Analysis.
Total Variance Explained of dependent variable
Comp
onent
Initial Eigenvalues
Total
% of Variance
.476
.379
Extraction Sums of Squared Loadings
Cumulative %
1
2.408
48.164
48.164
2
.851
17.020
65.184
3
.676
13.516
78.701
4
.562
11.238
89.938
5
.503
10.062
100.000
Total
2.408
% of Variance Cumulative %
48.164
48.164
Communalities of dependent variable after deleting item with little communality
Initial
Extraction
Perf1
1.000
Perf2
1.000
Perf3
1.000
Perf4
1.000
Extraction Method: Principal Component Analysis.
Total Variance Explained after deleting item with little communality
Comp
onent
Initial Eigenvalues
Total
% of Variance
.480
.591
.607
.468
Extraction Sums of Squared Loadings
Cumulative %
1
2.146
53.657
53.657
2
.786
19.658
73.315
3
.564
14.099
87.414
4
.503
12.586
100.000
Total
2.146
% of Variance Cumulative %
53.657
53.657
Correlations
EmployeeCRM
Collaborative
Client
performance Technology Customization relationship
CRM
performance
Pearson
Correlation
1
Sig. (2-tailed)
98
.094
.498**
.467**
.080
.000
.000
N
Collaborative Pearson
Technology Correlation
Sig. (2-tailed)
N
Customization Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
EmployeeClient
relationship
344
344
344
344
.094
1
.192**
.314**
.080
344
344
.000
344
.000
344
.498**
.192**
1
.385**
.000
344
.000
344
344
.000
344
.467**
.314**
.385**
1
.000
344
344
Sig. (2-tailed)
.000
.000
N
344
344
**. Correlation is significant at the 0.01 level (2-tailed).
Model Summary for multiple regression analysis
Change Statistics
R
Adjusted Std. Error of R Square
Square R Square the Estimate Change
F
Change df1
Sig. F
df2 Change
Model
R
1
.497a
.247
.245
.636
.247 112.483
1
342
.000
2
b
.382
.378
.577
.135 74.217
1
341
.000
.014
1
340
.005
.618
c
3
.629
.396
.391
.571
a. Predictors: (Constant), Customization
7.942
b. Predictors: (Constant), Customization, EmployeeClientRelationship
c. Predictors: (Constant), Customization, EmployeeClientRelationship, Communication
ANOVAd
Model
1
2
Sum of Squares
df
Mean Square
Regression
45.479
1
Residual
138.277
342
Total
183.756
343
Regression
70.195
2
Residual
113.561
341
99
F
45.479 112.483
Sig.
.000a
.404
35.097 105.390
.333
.000b
Total
183.756
343
Regression
72.787
3
24.262
Residual
110.969
340
.326
Total
183.756
a. Predictors: (Constant), Customization
343
3
74.338
.000c
b. Predictors: (Constant), Customization, EmployeeClientRelationship
c. Predictors: (Constant), Customization, EmployeeClientRelationship, Communication
d. Dependent Variable: CRM performance
Coefficientsa
Unstandardized
Coefficients
Model
1 (Constant)
B
Standardized
Coefficients
Std. Error
45.479
Beta
Collinearity
Statistics
t
Sig. Tolerance VIF
45.479 112.483 .000a
1
Customization
138.277
342
2 (Constant)
183.756
343
Customization
70.195
2
Employee-client
113.561
341
relationship
3 (Constant)
183.756
343
Customization
72.787
3
Employee-client
110.969
340
relationship
Communication 183.756
343
a. Dependent Variable: CRM performance
.404
35.097 105.390 .000b
.333
45.479
1
138.277 342
183.756 343
70.195
2
113.561 341
c
24.262 74.338 .000
.326
183.756 343
72.787
3
110.969 340
183.756 343
Excluded Variablesd
Collinearity Statistics
Model
1
Beta In
t
Partial Toleran
Minimum
Sig. Correlation
ce
VIF Tolerance
Technology
.104a 2.215 .027
.119
.995 1.005
.995
Communication
.255a 4.681 .000
.246
.697 1.435
.697
100
Employee-client
relationship
.376a 8.615 .000
.423
.951 1.052
.951
.011b
.259 .796
.014
.932 1.073
.890
.148b 2.818 .005
.151
.646 1.547
.646
3
Technology
-.021c -.475 .635
-.026
a. Predictors in the Model: (Constant), Customization
.871 1.148
.604
2
Technology
Communication
b. Predictors in the Model: (Constant), Customization, Employee-client relationship
c. Predictors in the Model: (Constant), Customization, Employee-client relationship,
Communication
d. Dependent Variable: CRM performance
Collinearity Diagnosticsa
Variance Proportions
Dimen Eigen Condition
Customiza Employee-client Communica
Model sion value
Index (Constant)
tion
relationship
tion
1
1
1.975
1.000
.01
.01
2
.025
8.871
.99
.99
1
2.887
1.000
.01
.01
.01
2
.088
5.728
.07
.10
.98
3
.025
10.757
.93
.89
.01
1
3.844
1.000
.00
.00
.01
.00
2
.093
6.444
.03
.05
.96
.04
3
.041
9.667
.41
.01
.00
.73
.022
13.128
.56
a. Dependent Variable: CRM performance
.94
.03
.23
2
3
4
101
AUTOBIOGRAPHY
Fullname:
Date of birth:
Nationality:
Address:
Cellphone:
Email:
TRUONG THI VAN ANH
Feb, 22nd 1987
Vietnamese
16 Le Co street, North Hoa Cuong ward
Hai Chau district, Danang city, Vietnam
+84 905 173767
[email protected]
[email protected]
Education background
University of Economics – The University of Danang, Danang city, Vietnam
 Bachelor of Economics in International Business Administration (August, 2008)
MIT Institute and University of Economics – The University of Danang, Danang city,
Vietnam
 Certificate of Sales representative (January, 2008)
Work Experience
Lecturer:






September, 2008 – Present
University of Economics – The University of Danang, Danang, Vietnam
Taking part in Customer Relationship Management (CRM) subject
Collaborator of “Vietnam traditional family in Quang Ngai” project in writing
theoretical basement
Supervisor of collaboration program between Commerce & Tourism Department
and ERIPT, supervisor of Danang Social Security research by University of
Economics
Clerk of “Baitho Plaza Model” project
Assistant of MUTRAP in documentary translation, assistant of “Area
Specialties” project, Danang Citi-gaz project in collecting and handling data
Supported specialist of Student assistance Office within 3 months in terms of
loan confirmation, lower tuition confirmation for poor-household students,
official correspondence, managed diplomas of in-service system students
Public Relations and Marketing Specialist:
102
July, 2008 – September, 2008
Vietnam Technological and Commercial Joint Stock Bank (TCB) - Danang branch,
Vietnam



Taking part in relationship marketing, preparing contents, making articles for
meetings about deployed program that commemorate the 15 years founding of
Vietnam TCB
Making statement about seat movement of new branches, ensuring deployment
of Quality month
Carrying out “Reporter Lens: Techcombank – Following historical line” column
and Cultural –Sporting Events, “Happy bet with award” program, making inner
Sport-contest articles
Salesperson:
December, 2007 – May, 2008
Internship at Techcombank, Danang branch
Achievements
Study and research
 Second Prize of “Students’ Scientific Research”, awarded by Vietnam Education
and Training Ministry, 2008
 Second Prize of “Vietnam Technology Innovation – VIFOTEC”, awarded by
Vietnam Foundation For Technology Innovation, 2008
 Second Prize of “Students’ Festival Scientific Research”, awarded by The
university of Danang, 2008
Publications
1. Le Van Huy and Truong Thi Van Anh (2009), “Economic Association based on
development of e-banking: TAM approach” at National Science Conference:
Economic Association on Central and Highland Area, pp. 295-305
2. Le Van Huy and Truong Thi Van Anh (2008), “Model of e-banking in Vietnam”
on Economics Studies Journal, No 7 (362), 48th Year, pp. 40-47
3. Le Van Huy and Truong Thi Van Anh (2008), “Developing Economics based on
development of e-banking: approach way from TAM” at Economic Association
Science Convention – “Central Economic area in the Middle”, pp. 169-178
4. Truong Thi Van Anh (2004), “Mama” in “The half century of Southern Students
on North (1954-2004)”, National Politics Publisher, pp. 354-356
103