Adoption trend for mobile banking in Urban Bangladesh

MOBILE BANKING IN URBAN

BANGLADESH: ADOPTION SCENARIO

AND CURRENT TRENDS

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ACKNOWLEDGEMENT

We would like to acknowledge to Mr Mahbub Jeshan, manager of trade marketing at Bkash

and Mr Shahedul Islam, Head of logistics of Dutch Bangla Bank Limited for providing us

with invaluable industry insight for the completion of the report. Finally, we express our

heartfelt gratitude to those who have cooperated with us in taking the surveys and helped us

complete the report through their encouragement.

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CONTENTS

Acknowledgement ................................................................................................................................... i

1 Introduction ..................................................................................................................................... 0

Background ............................................................................................................................. 0 1.1

Issues ....................................................................................................................................... 1 1.2

Research question ................................................................................................................... 1 1.3

Objectives ............................................................................................................................... 2 1.4

1.4.1 Broad objective ................................................................................................................... 2

1.4.2 Specific objectives .............................................................................................................. 2

Hypotheses .............................................................................................................................. 2 1.5

Rationale ................................................................................................................................. 3 1.6

Scope ....................................................................................................................................... 3 1.7

Limitations .............................................................................................................................. 4 1.8

Contribution of the research .................................................................................................... 4 1.9

2 Literature Review ............................................................................................................................ 5

Adoption process: Diffusion of innovation theory .................................................................. 7 2.1

3 Research type .................................................................................................................................. 8

4 Research Method: ........................................................................................................................... 9

Sample size determination ...................................................................................................... 9 4.1

Questionnaire Development .................................................................................................. 10 4.2

4.2.1 Questionnaire Design ........................................................................................................ 10

4.2.2 Questionnaire validity ....................................................................................................... 10

4.2.3 Questionnaire reliability .................................................................................................... 11

Data Collection ..................................................................................................................... 11 4.3

4.3.1 Primary DataCollection ..................................................................................................... 11

4.3.2 Secondary Data ................................................................................................................. 14

5 findings ......................................................................................................................................... 14

adoption scenario .................................................................................................................. 14 5.1

5.1.1 adoption summary ............................................................................................................. 14

5.1.2 Identification variables ...................................................................................................... 14

5.1.3 Interpretation ..................................................................................................................... 15

5.1.4 frequencies ........................................................................................................................ 15

5.1.5 Adopter profile .................................................................................................................. 15

sample statistics .................................................................................................................... 16 5.2

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5.2.1 Sample demography .......................................................................................................... 16

5.2.2 Access to supporting technology ...................................................................................... 17

5.2.3 Decision sources ............................................................................................................... 19

mean analysis ........................................................................................................................ 20 5.3

similarity test ......................................................................................................................... 21 5.4

Regression Analysis .............................................................................................................. 21 5.5

Hypothesis Testing ................................................................................................................ 24 5.6

5.6.1 Specific Objective 1: To identify the relations between consumer adoption of mobile

banking and user characteristics.. ................................................................................................. 25

5.6.2 Specific Objective 2: To analyze the perception of m-banking adopters regarding the

technology ..................................................................................................................................... 33

5.6.3 Specific Objective 3: To provide a holistic view on the industry trends. ......................... 36

Qualitative finding: industry trend ........................................................................................ 39 5.7

5.7.1 Growth trend ..................................................................................................................... 39

5.7.2 Market Players: ................................................................................................................. 40

6 recommendations .......................................................................................................................... 41

7 conclusion ..................................................................................................................................... 42

8 appendix ........................................................................................................................................ 43

Notes ..................................................................................................................................... 43 8.1

bibliography .......................................................................................................................... 44 8.2

9 Bibliography ................................................................................................................................. 44

10 Annexure ....................................................................................................................................... 45

Annex 1: Reliability Test ...................................................................................................... 45 10.1

Annex 2: Descriptive test ...................................................................................................... 47 10.2

10.2.1 Frequency ...................................................................................................................... 47

Annx 3: Similarity test .......................................................................................................... 57 10.3

Annex 4: Regression analysis ............................................................................................... 60 10.4

Annex 5: Correlation ............................................................................................................. 61 10.5

Annex 6: Schema .................................................................................................................. 62 10.6

Annex 7: Questionnaire ........................................................................................................ 63 10.7

Annex 8: KII Findings ............................................................................................................ 1 10.8

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EXECUTIVE SUMMARY

Electronic banking, internet banking, mobile banking, SMS banking these are the buzzwords

of the global financial world. Bangladesh, the 60th

ranked (in terms of Nominal GDP figure,

World Bank 2012) low income democracy in the South Asia, is no stranger to these

phenomena. Mobile banking services in emerging economies like Bangladesh has the

potential to bring the unbanked potential under the banking sector. Mobile banking is

basically the provision of banking and financial services, such as cash-in, cash out, merchant

payment, utility payment, salary disbursement, foreign remittance, government allowance

disbursement, ATM money withdrawal through mobile technology devices. It is usually

operated through a collaboration of commercial banks and mobile service providers. Mobile

banking operations in Bangladesh started in 2011 and currently there are two major mobile

banking service providers in Bangladesh: Dutch Bangla Bank mobile banking and bKash, a

concern of BRAC Bank. Apart from them, 15 other banks are providing this service on a

small scale. At present the number of mobile banking customers in the country has exceeded

five million and 95% of the market share belongs to bKash (80% almost) and DBBL.

To detect the adoption pattern, consumers were grouped into 5 major categories in the

adoption process: Innovator, Early adopter, Early majority, Late majority and Laggards. The

scope of this research was Dhaka city only. Convenience issue apart, the diversified

population in the capital city accounts for 40% of the total urban population of the country.

To ensure a balanced reach in all areas of the city, the users are classified into three

categories according to the geographic location of their residents: Central Dhaka, Newly

developed areas and Outskirts. Although the primary target market for m-banking is rural

Bangladesh but they have a lower access to IT enabled services. Since the initial target

market is not absolutely ready, urban market-having greater technological accessibility rate

can be an effective niche to popularize m-banking. Moreover the m-banking service

providers have their strong customer base already (of other services like ATM, e-banking,

regular banking etc.) in urban areas which will work as a primary determinant behind

introduction of m-banking here. There is no elaborative research paper online as on this

arena. Thus the issues like urban scenario of mobile banking option, current market forces

etc. needed vital attentions as to bridge the research gap.

A survey questionnaire was designed addressing several factors in the process and quota

based sampling was done to derive responses from different areas of the city. At 95%

confidence interval sample size was determined 384 but budget and time constraints allowed

196 responses. That adjusts our confidence interval at 91.12% for a 10% precision level.

Majority of the respondents were male, 25-31 years old, Graduate/Postgraduate, central

Dhaka residents, private service holders, low mid to mid income non-adopters of mobile

banking.

The dominant adopter group is early adopter in urban m-banking market. Innovators or

current adopters cover1/4th

of the potential market share. This suggests that the market is still

in its premature stage in Bangladesh and response generation will take a long time.

Demographic factors do not seem to have much impact on the adoption process, but access to

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supporting technology and literacy level of these technologies tends to affect the adoption. In

general, adopters have a negative user experience with m-banking and they still perceive it to

be a low safety technology. Majority of the banked customers complained about the time

consumption of traditional banking mode and the adopters perceive m-banking to be low

time-consuming. This advantage apart, m-banking has not yet been able to extend its

potential contributions to the market. This might be due to lack of motivated efforts by the

banks in this regard. Bkash apart, no other bank considers this section as a strategic business

unit and seem to be introducing the service only as a response to Bangladesh bank‟s green

banking policy.

Targeted value proposition, increased promotion, channel improvement, strategic alliance

with telecommunication companies have been recommended based on the evidences

extracted from international and local market case studies. With the proper utilization of the

consumer market understanding, the Banks are expected to augment their services in m-

banking section and benefit all the stakeholders immensely.

1 INTRODUCTION

BACKGROUND 1.1

Technology is changing the way that consumers connect with banks and with each other

presenting new opportunities for the previously unbanked. The sector is hopping from one

technology to the next to widen its access in a cost-effective and green way. Electronic

banking, internet banking, mobile banking, SMS banking these are the buzzwords of the

global financial world. Bangladesh, the 60th

ranked (in terms of Nominal GDP figure, World

Bank 2012) low income democracy in the South Asia, is no stranger to these phenomena.

Bangladesh has 4 State Owned Commercial Banks, 30 Private commercial banks, 4

specialized banks, 9 Foreign Commercial Banks and 4 non-scheduled banks 1under the

authority of its central bank, Bangladesh Bank, for serving the financial purposes of its

current population of 150,493,658( World bank, 2011). These 47 banks operate in 7772

branches spread countrywide. The banking sector has been experiencing rapid changes

because of numerous digitalization oriented initiatives encouraged by the govt. All banks

now are computerized at least at head office level. If we go at branch level only; 19%

branches of NCBs, 38% branches of PCBs and97 % branches of FCBs are computerized. An

ITRC survey found that overall computer density in the banking sector is 1.64. Majority of

banks is planning to introduce ICT for integration of banking services and new ICT based

banking services.

Foreign commercial banks are playing the pioneer role in introducing modern financial

products and services. Private Commercial Banks have started to follow the same pattern. On

the other hand Nationalized Commercial Banks and the Specialized Banks (SBs) could not

yet show notable performance regarding the issues. But due to the demands of the time, they

are now taking initiatives to launch modern and innovative products and services. Currently

most of the banks in Bangladesh are providing electronic products and services to their

Customers‟ .We cannot say they are completely following electronic way.

While banking sector is getting all electronic and carbon-free2, the penetration rate of

telecommunication has increased exponentially. In between 2003-2008, the number of mobile

subscribers increased by 97.8% in the region (UNESCAP Statistics Division, 2011). 60% of

the total population now has access to mobile phones. This one device has shaped the

economic and social condition of the country and its population by adding various layers to

its services. Communication, business, social cohesion etc. are some of the opportunities this

sector and its services have facilitated over the years. The total impact of mobile

1 Non- scheduled banks are those banks which are not registered under schedule of RBI act, 19

2 According to the carbon-finance unit of world bank, the general term is applied to investments in GHG

emission reduction projects.

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communications on Gross Domestic Product (GDP) has been, and continues to be, substantial

and it accounted for 2.1% of GDP in 2004 that increased up to 6.2%of GDP in 2007.

Rapid growth of mobile phone users and wider range of the coverage of Mobile Network

Operators (MNOs) have made their delivery channel an important tool‐of‐the‐trade for

extending banking services to the unbanked/banked population. To avail of this opportunity,

the banking sector has, under its umbrella of electronic-banking, introduced a new dimension

of services; mobile banking. The country now has 17 banks providing the mobile-banking

facilities both for the banked and unbanked population for ease of transaction and financial

inclusion.

ISSUES 1.2

Although millions of dollars have been spent on building mobile banking systems, reports on

mobile banking show that potential users may not be using the systems, despite their

availability. This is a recurring issue particularly for the low income, developing countries.

Beneath its shiny features, mobile banking is plagued with trust and security concern. The

half or uneducated Bangladeshis , even today, use mobile mainly as a communication tool

and are not very aware and/or interested in its value added services3. The urban population

has better access to amenities that facilitate adoption of latest technologies. However, if we

consider the standard of the amenities available in Bangladesh, we will not find anything

encouraging. But the country, like any other country cannot avoid the changes that are

turning the world upside down. As Bangladesh is slowly entering the field of advanced

banking technologies with its limited resources, it is becoming more important to actually

investigate and evaluate the real opportunities the sector and its services have given its still

developing population. Also, the fact that, only 36.17% (17 among 47) of the Banks have

invested in this technology, makes it clearer that thorough analysis of the potential consumer

market is essential to eradicate the uncertainties ( whether they will adopt or refuse) involved

for further investments.

RESEARCH QUESTION 1.3

The questions that stem from the context set above are:

Is the already facilitated urban Bangladesh ready to adopt (and thus become

customers from potential consumers) the advanced mobile banking technology?

3 According to a survey conducted by BIDS in 2009 on awareness of ICT in rural Bangladesh, 68% of the

respondents, most of which are women, do have own mobile phone. All of them use mobile phone mainly for communicating with friends and family members, the other uses include listening songs, watching videos, capturing picture, etc. Majority of the respondents are neither aware of nor use other mobile phone value-added services, such as money transfer, mobile banking, bill pay, internet browsing, medical and agriculture services etc.

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What is the nature of the usage pattern for existing customers of mobile banking?

What are the current trends in the sector in terms of growth and competition?

OBJECTIVES 1.4

1.4.1 BROAD OBJECTIVE

To find out the adoption pattern of m-banking in Urban Bangladesh and analyze its current

trend in the growing banking sector.

1.4.2 SPECIFIC OBJECTIVES

To identify the relationship between consumer adoption of m-banking and user

characteristics.

To analyze the perception of m-banking adopters regarding the technology.

To provide a holistic view on the industry trends.

HYPOTHESES 1.5

1. There is no relationship between age and consumer‟s adoption of m-banking.

2. There is no relationship between income and consumer‟s adoption of m-banking

3. There is no relationship between gender and consumer‟s adoption of m-banking.

4. There is no relationship between occupation and consumer‟s adoption of m-banking.

5. There is no relationship between educational qualification and consumer‟s adoption of

m-banking

6. There is no relationship between residence and consumer‟s adoption of mobile

banking.

7. There is no relationship between level of literacy about mobile functions in learning

m-banking and consumer‟s adoption of m-banking.

8. There is no relationship between the amount of money transacted and usage of m-

banking

9. Consumer‟s adoption of m-banking is not influenced by the views of social

networking websites.

10. Consumer‟s adoption of mobile banking is not influenced by the decision of family

members and peers.

11. Frequency of computer usage in personal life does not affect consumer adoption m-

banking.

12. Frequency of computer usage in professional life does not affect consumer adoption

of m-banking.

13. Frequency of internet usage in personal life does not affect consumer‟s adoption of m-

banking.

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14. Frequency of internet usage in professional life does not affect consumer‟s adoption

of m-banking.

15. Mobile banking users do not perceive the use of mobile banking to be time

consuming.

16. Mobile banking users do not perceive the learning process of mobile banking to be

complex.

17. Mobile banking users do not perceive the use of mobile banking to be risky.

18. Mobile banking users do not perceive the use of mobile banking to be costly

19. Mobile banking users do not perceive the use of mobile banking to be inconvenient.

20. Mobile banking users do not perceive the use of mobile banking to be difficult

21. Usage of mobile banking remains same during the time of political turmoil.

22. Usage of mobile banking remains same during the time of natural disaster

23. Usage of mobile banking remains same during the time of share market crisis.

24. Majority of people does not follow offline/direct cash transaction as their dominant

form of payment

RATIONALE 1.6

Mobile banking has been introduced in Bangladesh targeting rural people exclusively, with a

view to providing financial services to the unbanked communities efficiently and at an

affordable cost. However these rural communities are yet dependent on NGO loans and

Micro Finance Institutions. Since the initial target market is not absolutely ready, urban

market-having greater technological accessibility rate can be an effective niche to popularize

m-banking. Moreover the m-banking service providers have their strong customer base

already (of other services like ATM, e-banking, regular banking etc.) in urban areas which

will work as a primary determinant behind introduction of m-banking here. Hence, the

initiation of this research took place. Urban people usually have a very busy schedule which

does not allow them to waste time via traditional banking procedures. Added to that many of

their demographic segments are early innovators of any new technological advancement

introduced. On the other hand, unlike rural villagers the amount of money transected is much

higher by urban customers, so need for secured money transfer and mitigation of perceived

risk etc. are vital to adoption of mobile banking here. In a nutshell, mobile banking in Urban

Bangladesh is going to be a potential inclusion. But there is no elaborative research paper

online as on this arena. Thus the issues like urban scenario of mobile banking option, current

market forces and future prospect etc. need vital attentions as to bridge the research gap.

SCOPE 1.7

The research has been conducted on the mobile users of Dhaka city. Convenience issue apart,

the diversified population in the capital city accounts for 40% of the total urban population of

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the country. To ensure a balanced reach in all areas of the city, the users are classified into

three categories according to the geographic location of their residents.

1. Central Dhaka (Gulshan, Banani, Dhanmondi, Mohammadpur etc.)

2. Recently developed areas (Uttara sector 11, JoarShaharaetc.)

3. Outskirts (Keraniganj, Luter Char etc.)

LIMITATIONS 1.8

Though, the area of the research is extensive, there are some constraints which have limited

the scope of the research:

Time constraint is the key limitation in this research.

This is accompanied by financial constraints which will limit the scope of the

research.

The absence of sufficient secondary data in this field resulted in the reliance of

primary data for drawing suitable conclusions.

The consistent objectivity of the parameters is dependent on individual respondents‟

perceptions.

Some of the respondents were not interested towards the filling of the questionnaire

resulting in biased opinion which has affected the outcome.

CONTRIBUTION OF THE RESEARCH 1.9

All the key stakeholders with regard to m-banking service provision will be benefitted by this

report. Given our report has explored multiple dimension of m-banking adoption practices

and its most recent market dynamics, it will factor into constructive contribution for the

concerned stakeholders mentioned below:

1. Bangladesh Bank: Since Mobile banking works as the most effective tool as to

propel the financial inclusion of the unbanked population of the country, BB has

always put a special focus on it. It has been developing Policy report on m-banking

from 2010, published annually- where growth prospect and risk scenario of m-

banking is discussed under the paradigm of only Key informant interviews with m-

banking service providers and Mobile network operators. Since our report has covered

an analysis (with primary data collected from field level consumer survey) on the

adoption pattern of current m-banking subscribers, BB will definitely be helped by

this.

2. M-bankers: Though 26 banks have been provided with the license as to carry on m-

banking, only 17 have adopted the alternative financial service delivery line. Again,

out of these 17 banks only two have 95% market share though almost all of them have

started simultaneously, first mover advantage did not come into play in this sector.

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Moreover, except the two leading m-bankers (i.e. Bkash and DBBL) other banks have

not been making any substantial growth in their customer base though the market is

growing in every month, 12-15% growth in every month of 2013. Hence we can say

that most of the market players of m-banking service have failed to understand the

market dynamics, customers‟ adoption practices and urban trend in subscribing to m-

banking. Our report will most definitely help them tap the urban market with better

understanding of the market behavior.

3. MNO: Mobile network Operators play a vital role behind m-banking. Right now we

have 6 mobile network operators in the country (Robi,GP, Banglalink, Airtel ,

Citycell and Teletalk). Till now they have been working as partners of m-bankers as

to provide the network platform of the service. Now that government has introduced

payment gateway system4, all these MNOs might start their own m-banking systems

whereby no external partnership will be needed. If that comes into being, these MNOs

can analyze the urban market scenario with particular focus to the consumers‟

technology diffusion process.

2 LITERATURE REVIEW

For the past two decades, the banking sector has chosen several new service channels based

on the progress of information technology to respond to the changes in customer preferences

and needs. E-baking, m-banking, Automated Teller Machines etc. are few of these new

dimensions attached to banking sector jargons off late. Owing to Increasing competition

from non-banks and changes in demographic and social trends (Lederer, 2001); banks have

acknowledged the value to differentiate themselves from other financial institutions through

these avenues of new service distribution channels (Daniel, 1999) which is often termed as

virtual Banking. For example, E-banking has been continuously growing as a new industry

during the last decade, and now all banking transactions can be completed through internet

applications (Smith, 2008). With the success of e-banking technologies, m-banking ideas

have started taking pace in recent years, and the availability of a wider network of service

delivery has led to increasing adoption among consumers. M-banking, hence, is the most

innovative and user friendly addition to the list of convenient banking.(Servon, 2008)

Mobile Banking is a financial transaction conducted by logging on to a bank's website using a

cell phone, such as viewing account balances, making transfers between accounts, or paying

bills. This can be conducted through the internet browser on the phone, through a program

downloaded from the bank, or by text-message (SMS) (Karim, 2008). Mobile banking is an

application of m-commerce which enables customers to access bank accounts through mobile

devices to conduct and complete bank-related transactions such as balancing cheques,

checking account statuses, transferring money and selling stocks (hoan, 2010)defined mobile

banking as an innovative method for accessing banking services via a channel whereby the 4 The service that automates the payment transaction between the shopper and merchant. It is usually a third-

party service that is actually a system of computer processes that process, verify, and accept or decline credit card transactions on behalf of the merchant through secure Internet connections ( Bangladesh Bank, 2013)

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customer interacts with a bank using a mobile device. Across the developing countries,

millions of people rely on informal economic activity and local level networks to earn their

living. Most of these populations are from bottom of pyramid and they don‟t have access to

basic financial services/banks as access to them is costly and very limited. However, the

outstanding growth of mobile sector worldwide has created a unique opportunity to provide

social and financial services over the mobile network to those grass root level people. With

over 4 billion mobile cellular subscriptions worldwide, mobile network has the ability to

immediately offer mobile banking to 61% of the world population (Sultana, 2009).

In Bangladesh, Mobile banking has been introduced by Dutch Bangla Bank Limited in 2011.

With the combination of two most recent technological advancements – internet and mobile

phone, mobile banking (mobile data service) was enabled and now such wireless internet

based commercial transaction is being performed by several banks here, bringing about a

paradigm shift in technology acceptance of even bottom of the pyramid population.

Following several years of deliberations and ad hoc permissions on Mobile Financial

Services, the Department of Currency Management and Payment Systems of Banglades

h Bank issued “Guidelines on Mobile Financial Services (MFS) for the Banks” on 22 Sept

ember 2011 which were subsequently amended on 20 December 2011. These guidelines

state that only a bank‐led model will be permitted. For Bangladesh Bank this means

that a customer‟s account, termed "Mobile Account", will rest with the bank and will

be accessible through the customer‟s mobile device. This mobile account will be a non‐

chequing account classified separately from a standard banking account.

It is believed that m-banking will provide another new channel for banking services,

especially in Bangladesh; because here more than 60% people own mobile phones but only

13% people have a bank account. (Ahmad, Laukkanen and Lauronen, 2005). In a

survey(stratified, SS-200) done in 2008 across the capital city Dhaka , showed that only

45.6% people of age between 20 to 35 years are currently using mobile service (Smith, 2008).

Hence Strategic implications and customer perception of m-banking services needs to be

explored with a focus on the consumer value creation and a better understanding about the

customer-perceived value of m-banking services.

Some demographic variables have interrelations which might have influence on the adoption

of mobile banking. As Lee (2009) stated that the cognitive propensity of individuals to risk-

differs across culture. This means that the customers‟ acceptance of mobile banking may be

influenced by cultural differences. For instance, mobile internet service has been quite

popular in Japan (over 60 million users in 2003) especially for those young and single (i.e.,

unmarried) consumers (Scornavacca and Barnes, 2004) But In many technologically

developed countries like Norway, m-banking is yet to be popularized. Although millions of

dollars have been spent on building mobile banking systems, reports on mobile banking show

that potential users may not be using the systems, despite their availability. Mobile banking

adoption has become such inconsistent because many people argue that internet and other

technology based transaction is not safe, not practical and would lead to fraud. On the other

hand a lot of people think it safer, flexible in time and can be done anywhere and anytime

(Chowdhury&Ahmmad 2011). Mattila (2010) in his research found that most of mobile

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banking users have average age ranges from 23 years to 34 years. Similarly, Cheah et.al

(2011) found the age range of most of the mobile banking users is 20-25. In earlier studies in

this regard have provided interesting observations like , Wu and Wang (2005), in a study on

middle class populations, found that cost had minimal significant impact on the adoption of

mobile banking while perceived risk, compatibility and perceived usefulness have significant

influences. On the other hand Karnani (2009) argues that cost plays important role in

choosing mobile banking. Rotchanakitumanuai and Speece (2003) investigated why

corporate customers do not accept mobile banking, which can assist banks to implement this

self-service technology more efficiently. Many Thai banks are currently implementing

mobile banking. Banks that offer service via this channel claim that it reduces costs and

makes them more competitive (Servon, 2008). However, many corporate customers are not

highly enthusiastic about mobile banking. The interviews with Thai firms suggested that

security of the Internet is a major factor inhibiting wider adoption. Those already using

Internet banking seem to have more confidence that the system is reliable, whereas non-users

are much more service conscious, and do not trust financial transactions made via Internet

channels.

In line with the analysis above we can conclude that Bangladesh has high potential in terms

popularizing m-banking as the main stream banking process. Given the need to continue

to advance financial inclusion we can say that fully developed Mobile Financial Service

operations can reduce barriers of physical access and cost and over time enable a much

higher proportion of the population to use basic formal sector deposit and payment services.

Access into the formal system might eventually lead to product innovations in

insurance, credit, pensions and government payments that reach many millions of

Bangladeshis. Entry to the formal financial sector can lead to greater financial intermediation

and contribute to growth. But the adoption status of Mobile banking technology will depend

on Factors such as perceived usefulness (PU), perceived ease of use (PEOU), relative

advantages (RA) and personal innovativeness (PI) (Cheah et.al 2011) of the local

demography.

ADOPTION PROCESS: DIFFUSION OF 2.1

INNOVATION THEORY

In the book Diffusion of Innovations, Rogers suggests a total of five categories of adopters in

order to standardize the usage of adopter categories in diffusion research. The adoption of an

innovation follows an S curve when plotted over a length of time:

http://en.wikipedia.org/wiki/Sigmoid_function

8

Below we provide the theoretic categorization with definitions:

Adopter

category

Definition

Innovators Innovators are the first individuals to adopt an innovation. Risk tolerance has

them adopting technologies which may ultimately fail. Financial resources

help absorb these failures. (Rogers 1962 5th ed, p. 282)

Early

adopters

This is the second fastest category of individuals who adopt an innovation.

More discrete in adoption choices than innovators. Realize judicious choice of

adoption will help them maintain central communication position (Rogers

1962 5th ed, p. 283).

Early

Majority

Individuals in this category adopt an innovation after a varying degree of time.

This time of adoption is significantly longer than the innovators and early

adopters. Early Majority tend to be slower in the adoption process, have above

average social status, contact with early adopters, and seldom hold positions of

opinion leadership in a system (Rogers 1962 5th ed, p. 283)

Late

Majority

Individuals in this category will adopt an innovation after the average member

of the society. These individuals approach an innovation with a high degree of

skepticism and after the majority of society has adopted the innovation

Laggards Individuals in this category are the last to adopt an innovation. These

individuals typically have an aversion to change-agents and tend to be

advanced in age. Laggards typically tend to be focused on "traditions".

3 RESEARCH TYPE

We have taken the Deductive Research Approach. Deductive approach begins with general

concepts (such as theory, laws, principles etc.). Based on the concepts, a specific hypotheses

is formed which can be tested in order to support the general ideas (Trochim, 2006).An

http://en.wikipedia.org/wiki/Diffusion_of_innovations#CITEREFRogers1962_5th_ed

http://en.wikipedia.org/wiki/Early_adopters

http://en.wikipedia.org/wiki/Early_adopters



http://en.wikipedia.org/wiki/Opinion_leadership


9

Inductive reasoning works the other way around, it works from observation (or observations)

toward generalizations and theories. Inductive can be seen as a theory developing through

interpreting collected data (Elo & Kyngas, 2008). We started off with the literature review

and formed hypotheses based on the current trends of mobile banking adoption in

Bangladesh. The existing research works explaining the demographic and social

characteristics of the mobile banking users were also taken into account from literature

review. Based on these we formed our research objectives and methodology

4 RESEARCH METHOD:

SAMPLE SIZE DETERMINATION 4.1

Formula:

Sample size: n = NZ2pq/ (NE

2 + Z

2pq)

Where, N= population size

Z = confidence level (ex. 95% confidence = 1.96),

E = range of possible random error

p = estimated proportion of success

q = 1 – p, or estimated proportion of failure

Population:

Number of urban mobile users= 22,226,408

The sample size will be determined at a confidence level of 95% (z-value=1.96). The desired

precision is ±5%. The value of p and q is determined as 0.5 as the population is thought of

being un-biased, resulting in equal chances of having positive and negative responses during

survey.

Hence,

n = NZ2pq/

(NE2 + Z

2pq)

=( 22,226,408 x 1.962 x 0.5 x 0.5)/

(22,226,408 x 0.052) + (1.96

2 x 0.5 x 0.5)

= 384

BD population:150,493,658

Urban population: 27% of total

population

Urban mobile penetration rate: 54.7%

of urban population

10

However, due to budget and time constraint, our survey was limited to 196 responses. That

adjusts our confidence interval at 91.12% for a 10% precision level.

QUESTIONNAIRE DEVELOPMENT 4.2

4.2.1 QUESTIONNAIRE DESIGN

Individual Questionnaire: Based on our cognitive understanding of the research topic

through secondary research and group study, we developed the questionnaire. Since our

survey respondents were to comprise urban population as majority, who usually maintain a

busy schedule; h/she would not get enough time to answer any descriptive question. We put

short multiple choice questions with ratings inscribed to most so that instead of random

answering, the respondents have to think and rate in a synchronized manner. Personal

questions like „household income‟ or „name of the bank respondent is having an account at‟

were put later in the questionnaire to ensure that it‟s not too early to have the real answer

before we establish a rapport with the test participant/respondent.

We have used a co-ordination schema of 2 parameters and 6 complex variables. The complex

variables were further expanded into 23 simple variables. The questions were formed in

accordance with the variables. Both likert scale and semantial scale format along with

multiple choice questions were designed in the questionnaire. The respondents were asked to

rate each of the variables relevant to the features of mobile banking on a scale of 1 – 5. The

questions were formed in a way so that a score of 1 on each of the questions denoted the

lowest value whereas a score of 5 denoted the highest. Both the co-ordination schema and

the questionnaire have been included in the appendix section.

KII Questionnaire: A short semi structured questionnaire was prepared for conducting Key

Informant interviews in Bkash and Dutch Bangla Bank Limited. Sensitive questions for the

company management were avoided. The questions specifically addressed the following

aspects to get a holistic scenario of the present scenario of mobile banking adoption in Urban

Bangladesh:

1. Characterization of mobile banking adoption in both rural (initial target) and urban

region at present.

2. Factors contributing to the growth of mobile banking usage in the urban regions.

3. Strengths and weaknesses towards the growth of mobile banking adoption in the

urban market.

4. Prospects of mobile banking in Urban Bangladesh in the coming years.

4.2.2 QUESTIONNAIRE VALIDITY

11

We have chosen face validity method to measure the validity of the variables for the research.

Face Validity implies that the items chosen to measure a variable are logically related to it.

From the literature reviews, we have logically chosen six complex variables to measure the

present mobile banking adoption scenario. The variables are demographic factors, access to

supporting technology, decision sources, impacts of social and natural events, innovation

characteristics and bridge of familiarity. All these variables are logically related to measure

consumer adoption of mobile banking. The service is considered to be effective if the

customers think it fulfills all their banking needs, it is secure, social environment supports the

use of mobile banking and the innovation characteristics are simple and convenient.

Under the 6 complex variables there are 23 simple variables to measure the consumer

adoption of mobile banking. Each of the questions designed are directly and logically related

to the objectives of our research. For example- to measure the influence of social norms on

his/her decision of mobile banking adoption we asked How much do you rely on family

members and peers when adopting new technologies? To measure the influence of

communication channels specifically the socialmedia we asked Influence of social

networking websites on you-and the respondents rated the statement on a scale of 1-5. All the

items chosen to ask the respondents are logically related to the simple variables, their

corresponding complex variable and lead to measure the mobile banking adoption scenario

among the urban consumers at present.

4.2.3 QUESTIONNAIRE RELIABILITY

If a measure, when applied repeatedly to the same object under constant conditions, produces

the same result each time, it is called reliable. There are few techniques to measure reliability.

Reliability test

The reliability of data was verified using both Alpha and Split Half Technique. The data was

found to be:

Reliability Statistics

Cronbach's Alpha N of Items

.701 25

A Cronbach‟s Alpha of 0.70 and above is considered to be reliable. Our Cronbach‟s Alpha

based on standardized items is 0.701, indicating the reliability of data.

DATA COLLECTION 4.3

4.3.1 PRIMARY DATACOLLECTION

Individual Survey: We have conducted 196 field individual surveys as to collect the

data. Urban development pattern has occurred in different parts of the city following

varied direction. From a broader spectrum we have divided the capital city with

12

regards to development trend, into 3 primary categories: 1. Central Dhaka 2. Out

Skirts 3.Recently Developed Areas. Depending on the development scenario (Road,

Infrastructure development, Socio economic change in the last decade etc.) adoption

of anything new will not be homogeneous in these areas. We used to quota sampling

to assign a fixed number of surveys for each of these locations thus ensuring

participation from among all the varied regions of the urban environment. Our survey

conduction was designed like this:

13

Region Target Respondent

Profile

Touch Points Number of Individual

Surveys conducted

Central Dhaka 125

Gulshan Income : 1 lac+, Age: 40-

50,Corporate professional,

Doctor, Business man,

lawyer, Banker

Commercial Buildings, Premium Restaurants,

Social Clubs

Banani Income : 1 lac+, Age: 40-

50,Corporate professional,

Doctor, Business man,

lawyer, Banker

Commercial Buildings, Premium Restaurants,

Social Clubs

Dhanmondi Middle Income groups

Income: 50K-1 lac , Age :

30-40. University

Students, Age:18-25

University campuses, Super Stores, Local

markets, Bus Stands

Elephant Road Middle Income groups

Income: 50K-1 lac, Age :

30-40. University

Students, Age:18-25

University campuses, Super Stores, Local

markets, Bus Stands

Mohammadpur Lower Middle Income

groups, Income : 20-30K,

Age group: 25-35

Local Tea Stalls, Kach Bazars, Grocery Shops

Out Skirts 36

Keraniganj Lower class

professionals: Drives,

Shopkeepers, NGO field

workers

Local Bazars, Huts, Members office, NGO office

Luter Char Climate Migrants, Bottom

of the pyramid population

Households, Cultivation fields

Recently-Developed Areas 35

Uttara Sector 11 Income : 1 lac+, Age: 40-

50 , Corporate

professional, Doctor,

Business man, lawyer,

Banker, Achiever,

Super Markets, Chain Branded Stores,

Commercial complex

14

4.3.2 Secondary Data

The secondary sources of information were mostly journals, articles and other relevant

reports. The literature review section of the research paper was based on these secondary

sources of information. Reports from different renowned banks like DBBL, Brac were

studied to collect information for our research. A few books were also used to provide proper

guidelines to carry out the research. Besides, we also collected information from reliable

websites like that of Bangladesh Bureau of Statistics and CIA fact book.

5 FINDINGS

ADOPTION SCENARIO 5.1

5.1.1 ADOPTION SUMMARY

Of the 196 respondents, there are 119 who are familiar with the concept and the term. That

means 60.17% the respondents are aware of the technology. 77 respondents showed interest

to use mobile banking and among them only 41 (20.92% of total sample) uses mobile-

banking as a major source for banking activities.

As shown by the innovation adoption model, there are 5 major categories to consumer

adoption of a new product in the market. There is a sequential process to transform a

consumer‟s knowledge into interest that ultimately leads towards final adoption of the

technology in question. Given the scenario, we have analyzed the adoption pattern and

devised an adopter profile that shows which demographic portion of the population represents

which consumer category.

5.1.2 IDENTIFICATION VARIABLES

We used the responses to 3 questions that describe our 3 identification variables:

Awareness: Are you familiar with the term m-banking?

University Students

JoarShahara Lower Middle Income

groups, Income : 20-30K,

Age group: 25-35

Newly constructed Building sites, Service Centers

15

Interest: Would be interested in m-banking?

Adoption: Do you use m-banking as a major source for your banking activities?

5.1.3 INTERPRETATION

As the definitions suggest:

We label a consumer as an innovator if he/she has responded positively to all 3

components.

We label a consumer as an early adopter if he/she has awareness and interest but has

not adopted the technology yet. We also label the consumers early adopters who has

adopted but show a negative interest for their risk-aversion nature.

A consumer is labeled as early majority if despite the lack of awareness, shows a

quick response to awareness and is interested to use the technology in the future.

A late majority member has awareness, still has no interest, which is indicator to slow

response to adoption process.

A laggard has neither of the components and is the last group in the society to accept

a new trend.

5.1.4 FREQUENCIES

Category Frequency Percentage

Innovator 48 25.53%

Early adopter 71 37.7659%

Early majority 33 17.55%

Late majority 12 6.383%

Laggards 24 12.76%

We see that early adopters are dominant group in urban Bangladesh. And only 1/4th

of the

market has so far been penetrated by the m-banking providers. This shows the slow market

response towards the initiatives taken by the banks.

5.1.5 ADOPTER PROFILE

Adoption stage Profile

Innovator 25-38 age group, male, living in central Dhaka, Graduate and/or Postgraduate, higher

functionality with mobile, mid-income and with moderate transaction needs.

Early adopter Early adopters are dominant group in Urban Bangladesh. 18-31 age group, male, Central

Dhaka resident, Postgraduate, basic functionality of mobile, low mid to mid income

group, with low to moderate transaction needs.

Early majority 25-31 age bracket, male, Central Dhaka resident, Postgraduate, below 15000BDT

income, basic mobile functionality and with moderately higher transaction need.

Late majority Can be within any age group( no significant pattern for age is observed), male, central

Dhaka resident, mid-income, higher mobile functionality , Graduate, mid to higher

16

income group.

Laggard 39 to 45 years old, male, outskirts resident, Undergraduate , low income, low transaction

need and with very basic mobile functionality.

SAMPLE STATISTICS 5.2

5.2.1 SAMPLE DEMOGRAPHY

1. Age

The lowest age group is 18 to 24, that includes 17.6% of the respondents. The analysis shows

that dominant age group here is 25 to 31 (27.3% of the respondents) while only 9.1%

respondents are from 53 and higher age group.

Age distribution of adopters and non-adopters:

Clearly the non-adopters outnumber the adopters of m-banking in all age groups. 36.6% of

the adopters are in 25-31 age group, while similarity in frequency is shown by 32-38 and 39-

45 age group.

2. Gender:

69.4% of the total 196 respondents are male, whereas about 29.6% of the respondents are

female. There were also two missing values contributing to the remaining 1 percent of the

respondents.

Gender distribution of adopters and non-adopters:

Male are the dominant m-banking adopters. Among the 41 respondents who are adopters,

around 68.3% are male and around 31.7 % are female.

3. Occupation

Majority of our respondents were Private Service Holder which is about 21.4% of the total

respondents. The least number of respondents were housewives which comprises about 6.6%

of the total respondents. Teacher respondents were roughly around 15.8% whereas student

respondents were close around 14.3%

It can be clearly seen from the statistics above that among the 41 respondents who are the

adopters of mobile banking, about 36.6 % of the respondents are Private Service Holder and

the closest occupational group who are adopters are teachers who holds a handsome 24.4% of

17

the total respondents. The rest 39% of the adopters comprise of the remaining interviewed job

holder types such as Students (7.3%), Businessman (7.3%) and housewives being the least

with only 2.4% of the adopters.

Among the 141 respondents who were non-adopters, about 18.4% of the respondents were

private service holders, and the closest non-adopters‟ occupational group to the private

service holders are students who constitute about 17% of the total non-adopter group.

4. Residence:

61.2% of the total respondents are from central Dhaka. Among the 125 central Dhaka

residents, we find 21 as adopters, which is 16.8% of this segment. If we look into the 35

residents from newly developed regions in Dhaka, we can see that 20% of these respondents

are adopters and the adoption rate is slightly higher in outskirts (22.22% of the outskirts

residents).

5. Education:

The adopters outnumber the non-adopter in most of the educational level. Undergraduates are

mostly non adopters with 26.6%. Among the adopters, postgraduates dominate the list with

39.5% followed by graduates, undergraduates and HSC degree holders.

6. Income:

There is no clear distinction as to who dominates the lists in case of income earned. The

income range 15k-50k encomprises majority of the adopters and non-adopters with 45.7%

and 41.3% respectively. They are followed by the income range less than 15K with 31.4% for

the adopters and 34.9% for the non-adopters. Income range 50-100k follows suit with

respondents having an income of 100000 and above are both the lowest number of adopters

and non-adopters.

7. Transaction amount:

The adopters outnumber the non-adopter in most of the transaction level. People transacting

money in the range less than 5k are mainly adopters with 27%. The non-adopters in this

range make up 25% of the respondents. The range 5K-15K dominates among both adopters

and non-adopters with 32.4% and 26.7% respectively. The range 30-0 K and above 50K is

dominated by the non-adopters with 13.8% and 12.9% respectively. This shows that mainly

people with low to moderate transaction need adopt mobile-banking for their banking

activities.

5.2.2 ACCESS TO SUPPORTING TECHNOLOGY

18

1. Access to computer

a) Frequency of computer usage in professional life:

Among the 141 non-adopters, there is also a very high percentage (about 26.2%) of people

use computer in their professional lives and about 27% use very frequently. But it can also be

mentioned that among the non-adopters, about 20.6% of them cannot use computer in their

professional lives.

b) Frequency of computer usage in personal life:

Among the 196 respondents, about 22.4% respondents cannot use computer in their personal

lives whereas a high percentage of respondents (about 23%) use computer in their personal

lives frequently and about 25.5% use computer in their personal lives very frequently.

Among the 41 adopter respondents, around 31.7% respondents use computer frequently in

their personal lives and about 17.1% respondents use computer very frequently in their

personal lives. Only 14.6% of the respondents cannot use computer in their personal lives.

Among the 141 non-adopters, although about 22.7% of the respondents use computer in their

personal lives frequently and about 29.1% people very frequently, about 23.7% of the

respondents cannot use computer in their personal lives.

2. Access to internet

a) Frequency of internet usage in professional life:

Among the 196 respondents, about 23% respondents cannot use computer in their

professional lives whereas a high percentage of respondents (about 21.9%) use internet in

their professional lives frequently and about 24% use computer in their professional lives

very frequently.

Among the 41 adopters, around 26.8% respondents use internet frequently and 26.8% use

very frequently in their professional lives. A low percentage ( about 14.6%) of adopters

cannot use internet in their professional life.

b) Frequency of internet usage in personal life:

Among the 196 respondents, about 26.5% of the respondents replied that they cannot use

internet in their personal life. There is also a very high percentage of respondents, about

29.1% who use internet very frequently in their personal lives.

19

Among 41 adopter respondents, around 22% respondents use internet very frequently in their

personal lives and about 36.6% use internet frequently. There are also 19.5% adopter

respondents who cannot use internet in their personal lives.

Among the 141 non-adopters, although about 31.9% of the respondents use internet very

frequently in their personal lives, there are also 27.7% respondents who cannot use internet in

their personal lives.

3. Ability to use mobile functions

Among the 196 respondents, although about 35.7% of the respondents replied that they can

only make phone calls through their mobile device, a high 30.4% replied that they can use all

the functionalities of a mobile device.

From the 41 respondents who are adopters, about 73.3 % replied that they can use all the

functionalities of mobile phone whereas about 20% replied that they can use all the basics of

mobile devices.

Among the received responses of non-adopters, about 40% of them can only use phone calls

through their phone device and a merely 24.4% only can use all the functions of a mobile

phone.

5.2.3 DECISION SOURCES

1. Influence of social network

Among the 196 respondents, about 29.6% replied that they were not at all influenced by

social networks, whereas only 8.2% of the respondents replied that they are highly influenced

by social networks.

Among the 41 adopters of mobile banking, a low 9.8% replied that they are highly influenced

by mobile banking and a very high figure of 31.4% respondents replied that they are not

influenced by social networking sites.

From the 141 non-adopter responses, 30.5% of them are not at all influenced by social

networking sites whereas only a mere 8.5% are highly influenced by social networking sites

2. Influence of family and peer in decision making

Among the 196 respondents, around 30.1% replied that they are moderately influenced by

family and peer in decision making and about 18.4% who are highly influenced by family

and peers.

20

Among the 41 adopters, around 31.7 replied that they are indifferent to the influence of

family and peer in decision making and only a mere percentage of 12.2% replied that they are

not at all influenced by family and peers.

Among the 141 non-adopters, around 34.8% replied that they are moderately influenced by

peer and family in decision making and only 17.7% replied that they are highly influenced by

family and peers.

MEAN ANALYSIS 5.3

In order to find out the level of access to computer, internet and the influence of different

decision sources, we derived a 5-point scale where zero frequency was given a -2 value and

very high frequency was given a value of 2. Our mean analysis shows us:

N Minimum Maximum Mean Std. Deviation

Statistic Statistic Statistic Statistic Statistic

Frequency of computer use in

professional life

177 -2.00 2.00 .3220 1.51253


personal life

187 -2.00 2.00 .2299 1.51180

Frequency of internet use in

professional life

177 -2.00 2.00 .1751 1.54767


personal life

187 -2.00 2.00 .1872 1.61053

Influence of social network in

life

176 -2.00 2.00 -.5852 1.30651

Influence of family and peer in

decision making

184 -2.00 2.00 .3641 1.25145

Valid N (listwise) 160

While access to computer and internet both have a frequent direction among the respondents,

influence of social media as a decision source has a negative mean (low influence). Family

and peer is a viable decision source with a mean 0.3641, slightly higher than 0 (somewhat

rely on them).

Also, we calculate the mean values of the perception factors that were exclusive to the user:

N Minimum Maximum Mean Std. Deviation

Statistic Statistic Statistic Statistic Statistic

Influence of political turmoil in

m-banking usage

60 -1.00 1.00 .3167 .67627

Influence of share market crisis

in m-banking usage

59 -1.00 1.00 -.1695 .46060

Influence of Natural disaster in 60 -1.00 1.00 -.0333 .60971

21

m-banking usage

Time consumption in m-banking 89 -2.00 2.00 -.6966 1.13222

Compatability of mobile banking 92 -2.00 2.00 .5435 1.23514

Convenience of technology 93 -2.00 2.00 .7097 1.12849

Similarity with previous versions 90 -2.00 2.00 -.2444 1.12491

Safety in usage 90 -2.00 2.00 -.1000 1.26358

Cost associated with usage 89 -2.00 2.00 .6517 1.18802

Complexity of the technology 88 -2.00 2.00 -.2614 1.28201

Valid N (listwise) 51

It shows that, unlike political turmoil, no other social and natural events that we mentioned,

(natural disaster and share market crisis) increases the usage rate among the users. Users

perceive m-banking a low time consuming and low safety technology that has little similarity

with previous version (e-banking in this scenario). In general, the overall perception among

the users is somewhat negative.

SIMILARITY TEST 5.4

We performed independent sample t-test among the respondents to compare their mean

values over the access and decision factors and we saw that frequency of computer and

internet usage in professional and personal life, also the influence of social media and

primary social groups (family and friends) do not vary significantly among the adopters and

non-adopters.

We also conducted a gender-wise mean comparison for the perception issues among the

adopters. The mean values do not vary significantly between the groups. That means, be it

male or female, a typical m-banking adopter will reflect upon similar user experience of the

technology.

REGRESSION ANALYSIS 5.5

Linear Multiple Regression can be used to address a variety of research questions. Here we

used to determine how mobile banking adoption can be explained by our set of defined

variables.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

1 .569a .324 .189 .37725

Predictors: (Constant), Influence of family and peer in decision making, Frequency of

internet use in professional life, Gender, Monthly Transaction, Occupation, Residence,

22

Familiarity with the tern, Education, Influence of social network in life, Mobile actitivities,

Age, Income Range, Frequency of computer use in personal life, Frequency of internet use

in personal life, Frequency of computer use in professional life

Capital R is the simple correlation coefficient that tells us how strongly the multiple

independent variables are related to the dependent variable. R: the value of the co-efficient of

correlation for our case is +.569 which means that there‟s moderately a strong relationship in

the customer‟s adoption of mobile banking with the factors.

R2

: is the coefficient of determination. The R2 value indicates how much of the dependant

variable in this case the mobile banking adoption can be explained by the independent

variables (the factors). From the model summary R2 is 0.324 which implies that more than

30% of the overall adoption scenario can be explained by the factors determined for the

research.

Adjusted R2

: adjusted R2 gives the true dependency. As the number of variable increases,

the R value increases without showing true dependency. To mitigate the phenomenon

Adjusted R2

is used to show the true dependency. Here the model shows that the adjusted R2

is .189 which means that 18.9% of the time, the changes in dependant variable, in this case

the adoption of mobile banking can be explained by the changes in the independent variables.

ANOVA: It is the analysis of variance (the deviations in the dependent variable).

ANOVAa

Model Sum of

Squares

Df Mean

Square

F Sig.

1

Regression 5.120 15 .341 2.398 .007b

Residual 10.674 75 .142

Total 15.794 90

a. Dependent Variable: Mobile Banking user

b. Predictors: (Constant), Influence of family and peer in decision making, Frequency of

internet use in professional life, Gender, Monthly Transaction, Occupation, Residence,

Familiarity with the tern, Education, Influence of social network in life, Mobile activities,

Age, Income Range, Frequency of computer use in personal life, Frequency of internet use in

personal life, Frequency of computer use in professional life

23

Statistical significance

The F-ratio in the ANOVA table tests whether the overall regression model is a good fit

for the data. If the sig. value is less than .05 (.01,.0001, etc), then the variable is making a

significant unique contribution to the prediction of the dependent variable. If greater than

.05, then you can conclude that variable is not making significant unique contribution to

the prediction of your dependent variable. The table shows that the independent variables

statistically significantly predict the dependent variable, F(15, 75) = 2.398, p < .05 (i.e.,

the regression model is a good fit of the data).

Coefficientsa

Model Unstandardized

Coefficients

Standardize

d

Coefficients

T Sig.

B Std. Error Beta

1

(Constant) 2.295 .310 7.406 .000

Age .008 .037 .032 .227 .821

Gender -.066 .094 -.072 -.699 .486

Occupation -.010 .020 -.057 -.516 .608

Residence -.108 .061 -.201 -1.785 .078

Education -.045 .051 -.110 -.885 .379

Income Range -.010 .069 -.021 -.143 .887

Monthly Transaction .038 .043 .117 .874 .385

Familiarity with the

tern .263 .097 .300 2.712 .008

Mobile actitivities -.282 .069 -.549 -4.065 .000

Frequency of

computer use in

professional life

-.071 .074 -.257 -.966 .337

Frequency of

computer use in

personal life

.076 .068 .274 1.114 .269

Frequency of internet

use in professional

life

.074 .074 .274 1.004 .319

Frequency of internet

use in personal life -.030 .064 -.115 -.462 .645

Influence of social

network in life .090 .043 .281 2.091 .040

Influence of family

and peer in decision

making

-.002 .033 -.005 -.055 .957

24

Overall mobile banking adoption= 2.295+Bi * dependant variables

So the regression model is,

2.295 +0.008x1 – 0.066x2 -0.010x3 - 0.108x4 - 0.045x5 - 0.010x6 + 0.038x7 + 0.263x8 -

0.282x9-0.071x10+0.076x11+0.074x12-0.030x13+0.90x14-0.002x15

The beta co-efficient explains how strongly the variables are associated with the adoption of

mobile banking and the corresponding significance value show how assertive we can be

about the association.

HYPOTHESIS TESTING 5.6

We formed a set of 24 hypotheses and our findings were:

Sl. Null Hypothesis (H0) Decision

1. There is no relationship between age and consumer‟s adoption of m-

banking. Do not reject H0

2. There is no relationship between income and consumer‟s adoption of m-

banking Do not reject H0

3. There is no relationship between gender and consumer‟s adoption of m-

banking. Do not reject H0

4. There is no relationship between occupation and consumer‟s adoption of

m-banking. Reject H0

5. There is no relationship between educational qualification and

consumer‟s adoption of m-banking Do not reject H0

6. There is no relationship between residence and consumer‟s adoption of

mobile banking. Do not reject H0

7. There is no relationship between level of literacy about mobile functions

in learning m-banking and consumer‟s adoption of m-banking. Reject H0

8. There is no relationship between the amount of money transacted and

usage of m-banking Do not reject H0

9. Consumer‟s adoption of mobile banking is not influenced by the views

of social networking websites. Do not reject H0

10. Consumer‟s adoption of mobile banking is not influenced by the

decision of family members and peers. Do not reject H0

11. Frequency of computer usage in personal life does not affect consumer

adoption m-banking. Do not reject H0

12. Frequency of computer usage in professional life does not affect

consumer adoption of m-banking. Do not reject H0

13. Frequency of internet usage in personal life does not affect consumer‟s

adoption of m-banking. Do not reject H0

14. Frequency of internet usage in professional life does not affect

consumer‟s adoption of m-banking. Do not reject H0

15. Mobile banking users do not perceive the use of mobile banking to be

time consuming. Do not reject H0

16. Mobile banking users do not perceive the learning process of mobile

banking to be complex. Reject H0

25


risky. Reject H0


costly Reject H0


inconvenient. Reject H0


difficult Reject H0

21. Usage of mobile banking remains same during the time of political

turmoil. Do not reject H0

22. Usage of mobile banking remains same during the time of natural

disaster Do not reject H0

23. Usage of mobile banking remains same during the time of share market

crisis. Do not reject H0

24. Majority of people does not follow offline/direct cash transaction as their

dominant form of payment Reject H0

We now describe the findings from these tests.

5.6.1 SPECIFIC OBJECTIVE 1: TO IDENTIFY THE RELATIONS BETWEEN

CONSUMER ADOPTION OF MOBILE BANKING AND USER

CHARACTERISTICS..

Hypothesis 1: There is no relationship between age and consumer‟s adoption of m-banking

H0: There is no relationship between age and consumer‟s adoption of m-banking

H1: There is a relationship between age and consumer‟s adoption of m-banking

Chi-Square Tests

Value Df Asymp. Sig. (2-sided)

Pearson Chi-Square 5.587a 5 .348

Likelihood Ratio 6.000 5 .306

Linear-by-Linear

Association

.654 1 .419

N of Valid Cases 176

Interpretation: Given, P value (asymptotic significance .348) is greater than .05 , we don‟t

reject the null hypothesis

Decision: Thus, we don‟t have enough evidence to support that age and adoption of m-

banking are co-related.

26

Hypothesis 2: There is no relationship between income and consumer‟s adoption of m-

banking

H0: There is no relationship between income and consumer‟s adoption of m-banking

H1: There is a relationship between income and consumer‟s adoption of m-banking

Chi-Square Tests

Value df Asymp. Sig. (2-

sided)



Linear-by-Linear Association .247 1 .619


Interpretation: Given, P value (asymptotic significance .449) is greater than .05; we don‟t


Decision: Thus, we don‟t have enough evidence to support that income and adoption of m-


Hypothesis 3: There is no relationship between gender and consumer‟s adoption of m-

banking.

H0: There is no relationship between gender and consumer‟s adoption of m-banking

H1: there is a relationship between gender and consumer‟s adoption of m-banking

Chi-Square Tests

Value Df Asymp.

Sig. (2-

sided)

Exact Sig.

(2-sided)

Exact Sig.

(1-sided)

Pearson Chi-Square .015a 1 .904

Continuity

Correctionb

.000 1 1.000

Likelihood Ratio .015 1 .904

Fisher's Exact Test 1.000 .523

Linear-by-Linear

Association

.015 1 .904


Interpretation: Given, P value (asymptotic significance .904) is greater than .05 , we don‟t


27

Decision: Thus we don‟t have strong evidence to support that Gender and adoption of m-


Hypothesis 4: There is no relationship between occupation and consumer‟s adoption of m-

banking.

H0: There is no relationship between occupation and consumer‟s adoption of m-banking

H1: There is a relationship between occupation and consumer‟s adoption of m-banking

Chi-Square Tests

Value Df Asymp. Sig. (2-

sided)





Interpretation: Given, P value (asymptotic significance .027) is less than .05, we reject the

null hypothesis

Decision: Thus, we have enough evidence to support that Occupation and adoption of m-


Hypothesis 5:There is no relationship between educational qualification and consumer‟s

adoption of m-banking

H0: There is no relationship between education and consumer‟s adoption of m-banking

H1: There is a relationship between education and consumer‟s adoption of m-banking

Chi-Square Tests


sided)





Interpretation: Given, P value (asymptotic significance .423) is greater than .05, we don‟t


Decision: Thus we don‟t have enough evidence to support that Education and adoption of m-


28

Hypothesis 6: There is no relationship between residence and consumer‟s adoption of mobile

banking.

H0: There is no relationship between residence and consumer‟s adoption of m-banking

H1: There is a relationship between residence and consumer‟s adoption of m-banking

Chi-Square Tests


sided)

Pearson Chi-Square .466a 2 .792

Likelihood Ratio .450 2 .798



Interpretation: Given, P value (asymptotic significance .792) is greater than .0, we don‟t


Decision: Thus we don‟t have enough evidence to support that Residence and adoption of m-


Hypothesis 7: There is no relationship between level of literacy about mobile functions in

learning m-banking and consumer‟s adoption of m-banking.

H0: There is no relationship between level of literacy about mobile functions in learning m-

banking and Consumer‟s adoption of m-banking

H1: There is a relationship between level of literacy about mobile functions in learning m-

banking and Consumer‟s adoption of m-banking

Chi-Square Tests


sided)



Linear-by-Linear Association 12.942 1 .000


Interpretation: Given, P value (asymptotic significance .001) is less than .05, we reject the

null hypothesis

Decision: Thus, we have strong evidence to support that level of literacy about mobile

functions in learning m-banking and adoption of m-banking are co-related.

29

Hypothesis 8:There is no relationship between the amount of money transacted and usage of

m-banking

H0: there is no relationship between Amount of money transacted and Consumer‟s adoption

of m-banking

H1: there is a relationship between Amount of money transacted and Consumer‟s adoption of

m-banking

Chi-Square Tests


sided)



Linear-by-Linear Association 1.269 1 .260


Interpretation: Given, P value (asymptotic significance .563) is greater than .05; we don‟t


Decision: Thus, we don‟t have enough evidence to support that Amount of money transacted

and adoption of m-banking are co-related.

Hypothesis 9: Consumer‟s adoption of mobile banking is not influenced by the views of

social networking websites.

Null Hypothesis (H0): Consumer‟s adoption of mobile banking is not influenced by the

views of social networking websites.

Alternate Hypothesis (H1): Consumer‟s adoption of mobile banking is influenced by the

views of social networking websites.

Correlationa

Mobile

Banking user

Influence of

social network

in life

Mobile Banking user Pearson Correlation 1 .013

Sig. (2-tailed) .865

Influence of social network

in life

Pearson Correlation .013 1


a. Listwise N=170

30

Interpretation: The variables are positively correlated with correlation coefficient of .013.

The calculated significance level for two tailed test is .86, showing that the correlation is not

significant.

Decision: We cannot reject the null hypothesis, i.e. we don‟t have enough evidence to prove

that consumer‟s adoption of mobile banking is influenced by the views of social networking

websites.

Hypothesis 10: Consumer‟s adoption of mobile banking is not influenced by the decision of

family members and peers

Null Hypothesis (H0): Consumer‟s adoption of mobile banking is not influenced by the

decision of family members and peers.

Alternate Hypothesis (H1):Consumer‟s adoption of mobile banking is influenced by the

decision of family members and peers.

Correlationsa

Mobile

Banking user

Influence of

family and peer

in decision

making



Influence of family and peer

in decision making



a. Listwise N=176



significant.

Decision: We cannot reject the null hypothesis, i.e. we do not have enough evidence to prove

that consumer‟s adoption of mobile banking is influenced by the decision of family members

and peers.

Hypothesis 11: Frequency of computer usage in personal life does not affect consumer

adoption m-banking.

Null Hypothesis (H0): Frequency of computer usage in personal life does not affect

consumer adoption m-banking.

Alternate Hypothesis (H1):Frequency of computer usage in personal life affects consumer

adoption m-banking.

31

Correlationsa

Mobile

Banking user

Frequency of

computer use

in personal life

Mobile Banking user Pearson Correlation 1 -.015


Frequency of computer use

in personal life

Pearson Correlation -.015 1


a. Listwise N=177

Interpretation:The variables are negatively correlated with correlation coefficient of .015.


significant.


that frequency of computer usage in personal life affects consumer adoption m-banking.

Hypothesis 12: Frequency of computer usage in professional life does not affect consumer

adoption of m-banking.

Null Hypothesis (H0): Frequency of computer usage in professional life does not affect

consumer adoption of m-banking.

Alternate Hypothesis (H1): Frequency of computer usage in professional life affects

consumer adoption of m-banking.

Correlationsa

Mobile

Banking user

Frequency of

computer use

in professional

life



Frequency of computer use

in professional life



a. Listwise N=167



significant.

32


that frequency of computer usage in professional life affects consumer adoption of m-

banking.

Hypothesis 13: Frequency of internet usage in personal life does not affect consumer‟s


Null Hypothesis (H0): Frequency of internet usage in personal life does not affect

consumer‟s adoption of m-banking

Alternate Hypothesis (H1): Frequency of internet usage in personal life affects consumer‟s




significant.

Decision:We cannot reject the null hypothesis, i.e. we do not have enough evidence to prove

that frequency of internet usage in personal life affects consumer adoption of m-banking.

Hypothesis 14: Frequency of internet usage in professional life does not affect consumer‟s


Null Hypothesis (H0): Frequency of internet usage in professional life does not affect


Alternate Hypothesis (H1): Frequency of internet usage in professional life affects


Correlationsa

Mobile

Banking user

Frequency of

internet use in

professional

Correlationsa

Mobile

Banking user

Frequency of

internet use in

personal life




personal life



a. Listwise N=176

33

life




professional life



Listwise N=167



significant.

Decision:We cannot reject the null hypothesis, i.e. we do not have enough evidence to prove

frequency of internet usage in professional life affects consumer adoption of m-banking.

5.6.2 SPECIFIC OBJECTIVE 2: TO ANALYZE THE PERCEPTION OF M-

BANKING ADOPTERS REGARDING THE TECHNOLOGY

Hypothesis 15: Mobile banking users do not perceive the use of mobile banking to be time

consuming.

Null Hypothesis (H0): Mobile banking users do not perceive the use of mobile banking to be

time consuming.

Alternate Hypothesis (H1): Mobile banking users perceive the use of mobile banking to be

time consuming.

Correlationsa

Mobile

Banking user

Time

consumption in

m-banking



Time consumption in m-

banking



a. Listwise N=86

34

Interpretation: The variables are negatively correlated with correlation coefficient of .073.


significant.


that mobile banking users perceive the use of mobile banking to be time consuming.

Hypothesis 16:Mobile banking users do not perceive the learning process of mobile banking

to be complex.

Null Hypothesis (H0): Mobile banking users do not perceive the learning process of mobile

banking to be complex.

Alternate Hypothesis (H1): Mobile banking users perceive the learning process of mobile


b. Listwise N=86


The calculated significance level for two tailed test is 0.002, showing that the correlation is

significant.

Decision: We reject the null hypothesis, i.e. mobile banking users perceive the use of mobile


Hypothesis 17: Mobile banking users do not perceive the use of mobile banking to be risky.


risky


risky

Correlationsb

Mobile

Banking user

Complexity of

the technology

Mobile Banking user Pearson Correlation 1 .337**


Complexity of the

technology

Pearson Correlation .337**

1


Correlationsb

Mobile Banking

user

Safety in usage

Mobile Banking user Pearson Correlation 1 -.314**


35

Interpretation: The variables are negatively correlated with correlation coefficient of .314.


significant.


banking to be risky.

Hypothesis 18: Mobile banking users do not perceive the use of mobile banking to be costly


costly.


costly.

Correlationsb

Mobile

Banking user

Cost

associated with

usage



Cost associated with usage Pearson Correlation -.358**

1


Interpretation: The variables are negatively correlated with correlation coefficient of .0.358.


significant.


banking to be costly.

Hypothesis 19: Mobile banking users do not perceive the use of mobile banking to be

inconvenient.


inconvenient.


inconvenient.

Correlationsb

Safety in usage Pearson Correlation -.314**

1


b. Listwise N=88

36

Mobile

Banking user

Convenience of

technology



Convenience of technology Pearson Correlation -.346**

1




significant.


banking to be inconvenient.

Hypothesis 20: Mobile banking users do not perceive the use of mobile banking to be

difficult


difficult.


easy.

Correlationsb

Mobile

Banking user

Compatibility

of mobile

banking



Compatibility of mobile

banking

Pearson Correlation -.458**

1


b. Listwise N=89



significant.


banking to be difficult.

5.6.3 SPECIFIC OBJECTIVE 3: TO PROVIDE A HOLISTIC VIEW ON THE

INDUSTRY TRENDS.

Hypothesis 21:Usage of mobile banking remains same during the time of political turmoil.

37

Null Hypothesis (H0): Usage of mobile banking remains same during the time of political

turmoil

Alternate Hypothesis (H1):Usage of mobile banking changes during the time of political

turmoil

Correlationsa

Mobile

Banking user

Influence of

political

turmoil in m-

banking usage



Influence of political

turmoil in m-banking

usage



a. Listwise N=57



significant.

Decision: We do not reject the null hypothesis, i.e. we do not enough evidence to prove

usage of mobile banking changes during the time of political turmoil

Hypothesis 22: Usage of mobile banking remains same during the time of natural disaster

Null Hypothesis (H0): Usage of mobile banking remains same during the time of natural

disaster

Alternate Hypothesis (H1): Usage of mobile banking changes during the time of natural

disaster

Correlationsa

Mobile

Banking user

Influence of

Natural

disaster in m-

banking usage



Influence of Natural disaster

in m-banking usage



a. Listwise N=57

38



significant.

Decision: We do not reject the null hypothesis, i.e. we do not have enough evidence to prove

that usage of mobile banking changes during the time of natural disaster

Hypothesis 23: Usage of mobile banking remains same during the time of share market

crisis.

Null Hypothesis (H0): Usage of mobile banking remains same during the time of share

market crisis

Alternate Hypothesis (H1): Usage of mobile banking changes during the time of share

market crisis

Correlationsa

Mobile Banking

user

Influence of

share market

crisis in m-

banking usage



Influence of share market

crisis in m-banking usage



a. Listwise N=57



significant.

Decision: We do not reject the null hypothesis, i.e. we do not have enough evidence to prove

that usage of mobile banking changes during the time of share market crisis.

Hypothesis 24: Majority of people does not follow offline/direct cash transaction as their

dominant form of payment.

Null Hypothesis (H0): Majority of people does not follow offline/direct cash transaction as

their dominant form of payment

Alternate Hypothesis (H1): Majority of people follow offline/direct cash transaction as their

dominant form of payment

Interpretation: The mean score was 1.7747with a standard deviation of .41892.The

calculated significance level for two tailed test is 0.000 which is less than 0.05, showing there

is significant difference.

Result: We reject the null hypothesis, i.e. majority of people follow offline/direct cash

transaction as their dominant form of payment.

39

QUALITATIVE FINDING: INDUSTRY 5.7

TREND

5.7.1 GROWTH TREND

As discussed earlier, Mobile financial services began in 2010-2011, with the aim to spread

banking services among poor people, and to help villagers receive remittances from

expatriate relatives securely and without trouble. Scheduled banks responded to the central

bank‟s initiative with vibrant participation. At present the number of mobile banking

customers in the country has

exceeded five million. (Desk,

2013). BB has approved 26

banks to provide mobile

banking, while 17 of them

have already started the service

and as a result, the number of

clients reached 5.25m in April

this year. Around 60 lac

users/subscribers have been

noted on May 2013.

A total of Tk1.2bn is being

transacted daily through

mobile banking, according to

BB figures. BB data show that

the total number of transactions by mobile banking was more than 15m in April 2013,

compared to about 14m in March 2013. The total transaction value stood at Tk36.4bn in

April, which was Tk33.3bn in March (Mobile banking gaining popularity, 2013). Transaction

through mobile banking service has been increasing at a rate of 20% every month, the rate is

even faster in the urban regions of the country.

9093

30500

71093 83638

March '12 December'12 March '13 April'13

Number of M-banking agents

Number of M-banking agents

Another important criteria as to

project the rapid growth of m-

banking usage could be the

increasing number of

agents/distribution partners of m-

banking service providers.

According to Bangladesh Bank

Policy Paper 2012,only 9000

agents were operating in the

market at that period. Now 8o

thousand agents are catering the

need of both rural and urban

population.

40

5.7.2 MARKET PLAYERS:

1. Market Leader: With more than 40000 community-based agents all over the country,

bKash is facilitating financial inclusion for millions of unbanked people and is the

market leader for mobile financial services with 80%+ share. BRAC Bank/bKash has

also received a $10 million grant from the Bill and Melinda Gates Foundation and

technical assistance from

ShoreBank International to

support the launch of its MFS

services. BRAC Bank/bKash

also partners with BRAC to

identify and train new agents

in addition to receiving agents

from Robi and also searching

for agents directly.

2. Market Challenger:

DBBL launched its MFS

service branded “DBBL

Mobile” in March 2011 using

a technology platform from an

international vendor called

Sybase 365. Presently DBBL operates this as a separate platform from its core

banking system, but the two systems can be linked in the future. DBBL having its vast

banking network across the country as the growth driver; is acquiring the 2nd

place in

the market. It has currently 10% market share with its urban target group having the

most prominent growth.

3. Market Follower: Though there are 15 other banks operating in the market, only

Islami Bank Limited‟s m-cash is worth mentioning. Because other financial

institutions are adopting m-banking as part of Bangladesh Bank‟s Policy drive, not as

their own profit earning SBU, given the lower return on investment in this sector. As

opposed to this trend, Islami Bank has opened in December 2012, and within 2

months of its operation it has managed some 1,600 agents while nearly 30,000 people

have opened their mobile banking accounts. Currently they have 3% share of the

whole market. „Islamic philosophy‟ led banking has been the unique selling

proposition of this bank as to drive a sudden growth.

Among the 17 banks providing the m-banking service across the country few banks

mentioned below have the potential as to grow and cater larger market share with its alternate

service delivery chain (agents).

41

Bank Technology

Platform

Mobile Network Operator Partnership Launch Date

Trust Bank

Genweb2 Teletalk Aug‐10

Dutch Bangla Bank

Sybase 365 Airtel,

Banglalink

Citycell

GrameenPhone

May‐11

BRAC Bank/bKash

Fundamo

(Visa)

Banglalink

Robi

GrameenPhone

Jul‐11

Mercantile Bank SMG GrameenPhone Feb‐12

Bank Asia iPay Not required Mar‐12

Islami Bank (m-cash) SMG Teletalk

GP

Airtel

Dec 12

NCC Bank (sure cash) SMG GP May 13

6 RECOMMENDATIONS

The current rate of mobile adoption in urban Bangladesh is very low when it is compared

with the number of bank customers that have heard about mobile banking facilities. This

shows that, consumers are yet to embrace this innovation due to many factors which have

been described in the research framework. This also show that, been informed alone is not

enough to persuade customers to use the self-service but this has to do with their behavioral

intention to adopt the service. Also, the adopters have a somewhat negative perception

towards different aspects of the technology, which is again a red flag for the companies.

Given the scenario, we can propose some specific recommendations for the stakeholders in

the sector to receive to optimized benefits:

Developing a USP and thus positioning the specific m-banking offer provided the

bank in the consumer market. The rapid success of Islami Bank in the arena can be a

role model for banks to follow.

The market is becoming more competitive than ever. 9 more banks are in the line to

open up m-banking services and intensify the competition even more. Choosing to

serve market niche with very specific value propositions can be one way to survive

this competition. This niche strategy might be supported by the profile we prepared on

the consumer market.

42

Majority of the consumers in the market still perceive online banking as a higher

value product than mobile banking and m-banking mostly helps them with instant

money transactions (not all the banking activities). Widening the scope to utilize m-

banking and properly promoting the scope in the consumer market is also

recommended.

Banks need to reconsider their overall business strategy since they do not consider m-

banking section as a strategic business unit yet in most cases. This hampers the banks,

the market and is wastage of resources for no gain at all.

Nearly 40% of the respondents were not aware of the term and became clear about the

actual value proposition of a typical m-banking service while answering the questions.

This lack of awareness and proper product knowledge has to be removed through

intensive promotional efforts undertaken by the banks. Direct promotion channels

(personal promotion media) can be an excellent strategy to adapt that will ensure

maximum effective exposure.

Perceived level of safety with m-banking is negative. The security measures need to

be properly communicated so that this negative perception is reduced in the market.

The entire sector is in a green movement and the consumer segment has to be in

alignment with this movement too. Promoting green practices and incentivizing

adoption of greener banking activities can be considered by the banks.

Emphasizing on alternative value delivery channels (agents) can be a turnaround

strategy for the market followers.

Increasing strategic partnership with telecom companies can be another way to

increase flexibility of the service and thus become more market friendly.

7 CONCLUSION

It can be concluded that mobile banking in Bangladesh is only at its primitive stage

dominated by the private banks. The use of mobile banking is confined to a few consumer

segments. The risks associated with mobile banking are many, which the banks have to

model using sophisticated systems and extensive use of technology. The banks can focus on

strategic consumer groups to maximize its revenues from mobile banking. The experiences of

the global economies suggest that banks cannot avoid the m-banking phenomenon, but to

gain a competitive advantage, they must structure their business models to suit to local

conditions.

43

8 APPENDIX

NOTES 8.1

1. Mobile banking: Mobile banking is a system that allows customers of a financial

institution to conduct a number of financial transactions through a mobile device such

as a mobile phone or personal digital assistant.

2. Internet banking: Internet banking allows customers of a financial institution to

conduct financial transactions on a secure website operated by the institution, which

can be a retail or virtual bank, credit union or building society.

3. Electronic banking: The use of computers to carry out banking transactions such as

withdrawals through cash dispensers or transfer of funds at point of sale.

4. SMS banking: SMS banking is a type of mobile banking, a technology-enabled

service offering from banks to its customers, permitting them to operate selected

banking services over their mobile phones using SMS messaging.

5. Consumer adoption of technological innovations: Consumer adoption of

technological innovations is the process consumers uses to determine whether or not

to adopt innovation. This process is influenced by consumer characteristics, such as

personality traits and demographic or socioeconomic factors, the characteristics of the

new product, such as its relative advantage and complexity, and social influences,

such as opinion leaders.

6. Lickert scale: A Likert scale measures the extent to which a person agrees or

disagrees with the question. The most common scale is 1 to 5. Often the scale will be

1=strongly disagree, 2=disagree, 3=not sure,4=agree, and 5=strongly agree.

7. Semantics scale: Semantic differential is a type of a rating scale designed to measure

the connotative meaning of objects, events, and concepts. The connotations are used

to derive the attitude towards the given object, event or concept.

8. Linear multiple regression: Multiple linear regression attempts to model the

relationship between two or more explanatory variables and a response variable by

fitting a linear equation to observed data. Every value of the independent variable x is

associated with a value of the dependent variable y.

44

BIBLIOGRAPHY 8.2

9 BIBLIOGRAPHY

Rahman, S. S. (2011). Mobile Banking in Bangladesh. Unpublished Report, Daffodil

International University, Dhaka, Bangladesh.

Ondiege P. (2010). Mobile Banking in Africa: Taking the Bank to the People, Journal

of „Africa Economic Brief‟ , Vol. 1 Issue 8, p.1

Bangens,L. &Soderberg,B. (2008). Mobile Banking-Financial Service for the

Unbanked? Kista: Spider

Baten, M. A., Kamil, A. A. (2010). E-Banking of Economical Prospects in

Bangladesh. Journal of Internet Banking and Commerce, August 2010, vol. 15, no.2.

Retrieved March 19, 2012

Khatiwada, I., Pant, R., Karmacharya, H. (2010). Mobile Telephony and Mobile

Banking: Adoption, Issues and Potential Economic Impacts for Nepal. Nepal

Telecommunication Corporation. Retrieved March 19, 2012

Sultana, R. (2009). Mobile Banking: Overview of Regulatory Framework in

Emerging Markets. 4th Communication Policy Research, South Conference,

Negombo, Sri Lanka. Retrieved March 19, 2012

LaforetSylvie,LiXiaoyan, (2005), “Consumers‟ attitudes towards online and mobile

banking in China”, Volume 23 issue 5.

45

10 ANNEXURE

ANNEX 1: RELIABILITY TEST 10.1

a. List wise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha N of Items

.701 25

Item-Total Statistics

Scale Mean if Item

Deleted

Scale Variance if

Item Deleted

Corrected Item-Total

Correlation

Cronbach's Alpha if Item

Deleted

Age 15.4667 88.552 -.003 .716

Gender 17.1333 89.410 .063 .702

Residence 17.3333 89.238 .067 .702

Education 16.4667 75.267 .778 .649

Income Range 16.2000 80.600 .422 .677

Monthly Transaction 16.2000 80.171 .414 .677

Familiarity with the tern 17.4000 89.829 .047 .702

Mobile activities 16.0667 89.781 .006 .705

Mobile Banking user 17.2000 87.029 .330 .692


professional life

17.8000 67.600 .805 .624


personal life

18.0000 69.571 .834 .628


professional life

17.9333 66.781 .899 .615


personal life

18.0000 71.286 .749 .638

Influence of social network in

life

19.0000 73.143 .693 .646

Influence of family and peer in

decision making

18.3333 82.238 .280 .689

Case Processing Summary

N %

Cases Valid 15 7.7

Excludeda 181 92.3

Total 196 100.0

46

Influence of political turmoil in

m-banking usage

18.2667 95.067 -.349 .726

Influence of share market crisis

in m-banking usage

19.1333 86.695 .351 .691

Influence of Natural disaster in

m-banking usage

18.7333 94.495 -.417 .721

Time consumption in m-

banking

19.8000 86.886 .120 .701

Scale Mean if Item

Deleted

Scale Variance if

Item Deleted

Corrected Item-Total

Correlation

Cronbach's Alpha if Item

Deleted

Compatibility of mobile

banking

17.8667 88.981 -.016 .716

Convenience of technology 17.3333 87.810 .128 .699

Similarity with previous

versions

19.0000 85.857 .085 .710

Safety in usage 18.9333 94.495 -.227 .739

Cost associated with usage 17.7333 92.638 -.170 .724

Complexity of the technology 19.4667 92.267 -.149 .726

47

ANNEX 2: DESCRIPTIVE TEST 10.2

10.2.1 FREQUENCY

Age Frequency Percent Valid Percent Cumulative Percent

Valid 18 to 24 33 13.4 17.6 17.6

25 to 31 51 20.7 27.3 44.9

32 to 38 32 13.0 17.1 62.0

39 to 45 32 13.0 17.1 79.1

46 to 52 22 8.9 11.8 90.9

53 and above 17 6.9 9.1 100.0

Total 187 76.0 100.0

Missing System 59 24.0

Total 246 100.0

Age adopter

Frequency Percent Valid Percent Cumulative Percent

Valid 18 to 24 5 12.2 12.2 12.2

25 to 31 15 36.6 36.6 48.8

32 to 38 9 22.0 22.0 70.7

39 to 45 7 17.1 17.1 87.8

46 to 52 2 4.9 4.9 92.7

53 and above 3 7.3 7.3 100.0

Total 41 100.0 100.0

Age non-adopter


Valid 18 to 24 26 18.4 19.3 19.3

25 to 31 33 23.4 24.4 43.7

32 to 38 22 15.6 16.3 60.0

39 to 45 22 15.6 16.3 76.3

46 to 52 19 13.5 14.1 90.4

53 and above 13 9.2 9.6 100.0

Total 135 95.7 100.0


Total 141 100.0

48

Occupation


Valid Student 28 14.3 15.1 15.1

Teacher 31 15.8 16.8 31.9

Private service holder 42 21.4 22.7 54.6

Businessman 26 13.3 14.1 68.6

Housewife 13 6.6 7.0 75.7

Government service holder 21 10.7 11.4 87.0

other 24 12.2 13.0 100.0

Total 185 94.4 100.0


Total 196 100.0

Occupation non adopter


Valid Student 24 17.0 17.9 17.9

Teacher 16 11.3 11.9 29.9

Private service holder 26 18.4 19.4 49.3

Businessman 22 15.6 16.4 65.7

Housewife 12 8.5 9.0 74.6

Government service holder 18 12.8 13.4 88.1

other 16 11.3 11.9 100.0

Total 134 95.0 100.0


Gender


Valid M 136 69.4 70.1 70.1

F 58 29.6 29.9 100.0

Total 194 99.0 100.0


Total 196 100.0

Gender adopter


Valid M 28 68.3 68.3 68.3

F 13 31.7 31.7 100.0

Total 41 100.0 100.0

Gender non-adopter


Valid M 97 68.8 69.3 69.3

F 43 30.5 30.7 100.0

Total 140 99.3 100.0

Missing System 1 .7

Total 141 100.0

49

Total 141 100.0

Frequency

Percent Valid Percent Cumulative Percent

Valid Central Dhaka 120 61.2 65.6 65.6

Newly developed 31 15.8 16.9 82.5

Outskirts 32 16.3 17.5 100.0

Total 183 93.4 100.0


Total 196 100.0

Residence adopter




Outskirts 8 19.5 20.5 100.0

Total 39 95.1 100.0


Total 41 100.0

Residence

non-adopter




Outskirts 21 14.9 15.9 100.0

Total 132 93.6 100.0


Total 141 100.0

Education


Valid <HSC 17 8.7 9.2 9.2

>HSC 46 23.5 24.9 34.1

Occupation adopter


Valid Student 3 7.3 7.3 7.3

Teacher 10 24.4 24.4 31.7

Private service holder

15 36.6 36.6 68.3

Businessman 3 7.3 7.3 75.6

Housewife 1 2.4 2.4 78.0

Government service holder

3 7.3 7.3 85.4

other 6 14.6 14.6 100.0

Total 41 100.0 100.0

50

Grad 51 26.0 27.6 61.6

Postgrad 69 35.2 37.3 98.9

Above 2 1.0 1.1 100.0

Total 185 94.4 100.0


Total 196 100.0

Education

adopter


Valid Below or just HSC 4 9.8 10.5 10.5

Undergraduate 5 12.2 13.2 23.7

Grad 13 31.7 34.2 57.9

Postgrad 15 36.6 39.5 97.4

Above 1 2.4 2.6 100.0

Total 38 92.7 100.0


Total 41 100.0

Education non adopter


Valid Below or just HSC

13 9.2 9.4 9.4

Undergraduate

37 26.2 26.6 36.0

Grad 38 27.0 27.3 63.3

Postgrad 50 35.5 36.0 99.3

Above 1 .7 .7 100.0

Total 139 98.6 100.0


Total 141 100.0


Valid <15k 56 28.6 33.7 33.7

15k-50k 71 36.2 42.8 76.5

50k-100k 28 14.3 16.9 93.4

>100000 11 5.6 6.6 100.0

Total 166 84.7 100.0


51

Income Range adopter


Valid <15k 11 26.8 31.4 31.4

15k-50k 16 39.0 45.7 77.1

50k-100k 4 9.8 11.4 88.6

>100000 4 9.8 11.4 100.0

Total 35 85.4 100.0


Total 41 100.0

Monthly Transaction


Valid <5k 39 19.9 24.7 24.7

5k-15k 46 23.5 29.1 53.8

15k-30k 37 18.9 23.4 77.2

30k- 50k 18 9.2 11.4 88.6

>50k 18 9.2 11.4 100.0

Total 158 80.6 100.0


Total 196 100.0

Monthly Transaction adopter

Frequency Percent Valid Percent

Cumulative Percent

Valid <5k 10 24.4 27.0 27.0

5k-15k 12 29.3 32.4 59.5

15k-30k 10 24.4 27.0 86.5

30k- 50k 2 4.9 5.4 91.9

>50k 3 7.3 8.1 100.0

Total 37 90.2 100.0


Total 41 100.0

Total 196 100.0

Income Range nonadopter


Valid <15k 44 31.2 34.9 34.9

15k-50k 52 36.9 41.3 76.2

50k-100k 23 16.3 18.3 94.4

>100000 7 5.0 5.6 100.0

Total 126 89.4 100.0


Total 141 100.0

52

Monthly Transaction

non- adopter


Valid <5k 29 20.6 25.0 25.0

5k-15k 31 22.0 26.7 51.7

15k-30k 25 17.7 21.6 73.3

30k- 50k 16 11.3 13.8 87.1

>50k 15 10.6 12.9 100.0

Total 116 82.3 100.0


Total 141 100.0

Access to supporting technology

Access to computer

Frequency of computer use in professional life


Valid Cannot use 40 20.4 22.6 22.6

Irregular 13 6.6 7.3 29.9

Indifferent 23 11.7 13.0 42.9

Frequent 52 26.5 29.4 72.3

Very frequent 49 25.0 27.7 100.0

Total 177 90.3 100.0


Total 196 100.0

Frequency of computer use in professional life(

adopter)



Irregular 3 7.3 7.5 22.5

Indifferent 7 17.1 17.5 40.0

Frequent 13 31.7 32.5 72.5

Very frequent 11 26.8 27.5 100.0

Total 40 97.6 100.0


Total 41 100.0

Frequency of computer use in professional life(

non-adopter)



Irregular 10 7.1 7.9 30.7

Indifferent 13 9.2 10.2 40.9

Frequent 37 26.2 29.1 70.1

Very frequent 38 27.0 29.9 100.0

Total 127 90.1 100.0


Total 141 100.0

53

b) Frequency of computer usage in personal life:

Frequency of computer use in personal life


Valid Can not use 44 22.4 23.5 23.5

Irregular 14 7.1 7.5 31.0

Indifferent 34 17.3 18.2 49.2

Frequent 45 23.0 24.1 73.3

Very frequent 50 25.5 26.7 100.0

Total 187 95.4 100.0


Total 196 100.0


( adopters)



Irregular 2 4.9 4.9 19.5

Indifferent 13 31.7 31.7 51.2

Frequent 13 31.7 31.7 82.9

Very frequent 7 17.1 17.1 100.0

Total 41 100.0 100.0


(non-adopters)



Irregular 12 8.5 8.8 33.1

Indifferent 18 12.8 13.2 46.3

Frequent 32 22.7 23.5 69.9

Very frequent 41 29.1 30.1 100.0

Total 136 96.5 100.0


Total 141 100.0

Access to internet

Frequency of internet use in professional life



Irregular 16 8.2 9.0 34.5

Indifferent 26 13.3 14.7 49.2

Frequent 43 21.9 24.3 73.4

Very frequent 47 24.0 26.6 100.0

Total 177 90.3 100.0


Total 196 100.0


a

54


Valid Can not use 6 14.6 15.0 15.0

Irregular 5 12.2 12.5 27.5

Indifferent 7 17.1 17.5 45.0

Frequent 11 26.8 27.5 72.5

Very frequent 11 26.8 27.5 100.0

Total 40 97.6 100.0


Total 41 100.0


a (non-adopters)



Irregular 11 7.8 8.7 35.4

Indifferent 17 12.1 13.4 48.8

Frequent 30 21.3 23.6 72.4

Very frequent 35 24.8 27.6 100.0

Total 127 90.1 100.0


Total 141 100.0

Frequency of internet use in personal life



Irregular 14 7.1 7.5 35.3

Indifferent 25 12.8 13.4 48.7

Frequent 39 19.9 20.9 69.5

Very frequent 57 29.1 30.5 100.0

Total 187 95.4 100.0


Total 196 100.0


a (adopters)



Irregular 2 4.9 5.0 25.0

Indifferent 6 14.6 15.0 40.0

Frequent 15 36.6 37.5 77.5

Very frequent 9 22.0 22.5 100.0

Total 40 97.6 100.0


Total 41 100.0


a(non-adopters)



Irregular 12 8.5 8.8 37.5

Indifferent 17 12.1 12.5 50.0

Frequent 23 16.3 16.9 66.9

55

Very frequent 45 31.9 33.1 100.0

Total 136 96.5 100.0


Total 141 100.0

Ability to use mobile functions

Mobile activities


Valid Phone calls 41 20.9 35.7 35.7

All basics 39 19.9 33.9 69.6

Higher functionality 35 17.9 30.4 100.0

Total 115 58.7 100.0


Total 196 100.0

Mobile activities

(adopters)



All basics 3 7.3 20.0 26.7


Total 15 36.6 100.0


Total 41 100.0

Mobile activities

a (non-adopters)



All basics 32 22.7 35.6 75.6


Total 90 63.8 100.0


Total 141 100.0

Decision sources

Influence of social network in life


Valid None 58 29.6 33.0 33.0

Low 41 20.9 23.3 56.3

Indifferent 39 19.9 22.2 78.4

Moderate 22 11.2 12.5 90.9

High 16 8.2 9.1 100.0

Total 176 89.8 100.0


Total 196 100.0

56

Influence of social network in life a (adopters)


Valid None 13 31.7 32.5 32.5

Low 10 24.4 25.0 57.5

Indifferent 10 24.4 25.0 82.5

Moderate 3 7.3 7.5 90.0

High 4 9.8 10.0 100.0

Total 40 97.6 100.0


Total 41 100.0

Influence of social network in life a(non-adopters)


Valid None 43 30.5 33.1 33.1

Low 30 21.3 23.1 56.2

Indifferent 29 20.6 22.3 78.5

Moderate 16 11.3 12.3 90.8

High 12 8.5 9.2 100.0

Total 130 92.2 100.0


Total 141 100.0

Influence of family and peer in decision making


Valid None 22 11.2 12.0 12.0

Low 20 10.2 10.9 22.8

Indifferent 47 24.0 25.5 48.4

Moderate 59 30.1 32.1 80.4

High 36 18.4 19.6 100.0

Total 184 93.9 100.0


Total 196 100.0

Influence of family and peer in decision making a (adopters)


Valid None 5 12.2 12.2 12.2

Low 6 14.6 14.6 26.8

Indifferent 13 31.7 31.7 58.5

Moderate 8 19.5 19.5 78.0

High 9 22.0 22.0 100.0

Total 41 100.0 100.0

Influence of family and peer in decision making a( non-adopters)


Valid None 15 10.6 11.1 11.1

Low 14 9.9 10.4 21.5

Indifferent 32 22.7 23.7 45.2

Moderate 49 34.8 36.3 81.5

High 25 17.7 18.5 100.0

Total 135 95.7 100.0


Total 141 100.0

57

ANNX 3: SIMILARITY TEST 10.3

Independent sample t-test for

comparing the mean values

adoptionwise

Levene's Test

for Equality of

Variances

F Sig. t df Sig. (2-tailed) 95% Confidence

Interval of the

Difference

Lower Upper

Frequency of

computer use in

professional life

Equal

variances

assumed

2.633 .107 .534 165 .594 -.39262 .68396

Equal

variances not

assumed

.566 72.310 .573 -.36691 .65824

Frequency of

computer use in

personal life

Equal

variances

assumed

9.257 .003 .196 175 .845 -.47481 .57954

Equal

variances not

assumed

.221 81.079 .826 -.41990 .52463

Frequency of

internet use in

professional life

Equal

variances

assumed

2.350 .127 .841 165 .402 -.31653 .78582

Equal

variances not

assumed

.891 72.301 .376 -.29023 .75952

Frequency of

internet use in

personal life

Equal

variances

assumed

5.363 .022 .715 174 .476 -.36279 .77456

Equal

variances not

assumed

.773 72.443 .442 -.32477 .73654

Influence of social

network in life

Equal

variances

assumed

.151 .699 -.171 168 .865 -.50689 .42612

Equal

variances not

assumed

-.172 65.500 .864 -.50919 .42843

Influence of family

and peer in decision

making

Equal

variances

assumed

.143 .706 -.739 174 .461 -.60042 .27341

58

Equal

variances not

assumed

-.715 63.068 .477 -.62043 .29342

Levene's Test

for Equality of

Variances

F Sig. t df Sig. (2-tailed) 95% Confidence Interval of the Difference

Lower Upper

Complexity of the

technology

Equal

variances

assumed

.085 .772 .846 85 .400 -.35469 .88051

Equal

variances not

assumed

.834 37.826 .410 -.37572 .90153

Cost associated with

usage

Equal

variances

assumed

1.020 .315 -.875 87 .384 -.81348 .31604

Equal

variances not

assumed

-.904 43.820 .371 -.80323 .30579

Safety in usage Equal

variances

assumed

.869 .354 1.057 88 .293 -.27998 .91635

Equal

variances not

assumed

1.017 38.079 .316 -.31536 .95172

Similarity with

previous versions

Equal

variances

assumed

.388 .535 .602 88 .549 -.36800 .68800

Equal

variances not

assumed

.634 48.574 .529 -.34732 .66732

Compatability of

mobile banking

Equal

variances

.054 .816 .988 89 .326 -.28906 .86118

59

assumed

Equal

variances not

assumed

1.015 45.718 .316 -.28142 .85354

Convenience of

technology

Equal

variances

assumed

.069 .793 -.333 90 .740 -.61544 .43872

Equal

variances not

assumed

-.341 45.081 .735 -.61054 .43382

Time consumption

in m-banking

Equal

variances

assumed

.185 .668 -.501 87 .618 -.66741 .39866

Equal

variances not

assumed

-.506 44.800 .615 -.66906 .40031

Influence of political

turmoil in m-

banking usage

Equal

variances

assumed

.625 .432 -1.201 57 .235 -.62017 .15519

Equal

variances not

assumed

-1.155 27.419 .258 -.64516 .18018

Influence of share

market crisis in m-

banking usage

Equal

variances

assumed

1.895 .174 1.331 57 .188 -.08832 .43846

Equal

variances not

assumed

1.310 28.715 .201 -.09835 .44849

Influence of Natural

disaster in m-

banking usage

Equal

variances

assumed

.019 .892 -1.095 57 .278 -.53471 .15656

Equal

variances not

assumed

-1.096 29.689 .282 -.54166 .16351

60

ANNEX 4: REGRESSION ANALYSIS 10.4

Model Summary

Mode

l

R R

Square

Adjusted R

Square

Std. Error of

the Estimate

1 .569a .324 .189 .37725

a. Predictors: (Constant), Influence of family and peer in decision making, Frequency of internet use in

professional life, Gender, Monthly Transaction, Occupation, Residence, Familiarity with the tern, Education,

Influence of social network in life, Mobile activities, Age, Income Range, Frequency of computer use in personal

life, Frequency of internet use in personal life, Frequency of computer use in professional life

ANOVAa

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 5.120 15 .341 2.398 .007b

Residual 10.674 75 .142

Total 15.794 90

Coefficientsa

Model 95.0% Confidence Interval for B

Lower Bound Upper Bound

1 (Constant) 1.678 2.912

Age -.066 .082

Gender -.253 .121

Occupation -.050 .029

Residence -.229 .013

Education -.147 .057

Income Range -.148 .128

Monthly Transaction -.049 .125

Familiarity with the tern .070 .455

Mobile activities -.420 -.144

Frequency of computer use in professional

life

-.218 .075

Frequency of computer use in personal life -.060 .212

Frequency of internet use in professional

life

-.073 .222

Frequency of internet use in personal life -.158 .099

Influence of social network in life .004 .176

Influence of family and peer in decision

making

-.067 .064

61

Assumption Testing

ANNEX 5: CORRELATION 10.5

Time

consuming

Complex Risk Costly Convenient Compatibility

User

Perception

on mobile

banking

-.073 .337 -.314 -.318 -.346 -.458

Computer

usage in

professional

life

Computer

usage in

personal life

Internet

usage in

professional

life

Internet

usage in

personal life

Influence of

social

networking

sites

Influence of

family and

peers

Mobile

banking

adoption

-.042 .015 -.065 -.054 .013 .056

Political turmoil increase Increase in natural

disaster

Share market crisis

Mobile banking usage .093 .105 .027

62

ANNEX 6: SCHEMA 10.6

Parameter Complex Variable Simple Variable Value Question no.

Consumer

Demographic factor Gender Male

Female

Question 16(ii)

Age 18-24

25-31

32-38

39-45

46-52

53 and above

Question 16(i)

Education Below HSC

Undergraduate

Graduate

Post graduate

Above

Question 16(iii)

Profession Checklist Question 16(iv)

Income Checklist Question 16(v)

Transaction need Checklist Question 16(vi) Question 16(iii)

Residence

Access to supporting

technology

Ability to use mobile functions Checklist of

activities

Question 4

Access to computer Frequency

checklist

Question 6

Internet literacy Frequency

checklist

Question 7

Decision sources

Influence of social networking media Yes/No checklist Question 8, 9

Influence of word of mouth promotion 5 point

likertscale

Question 10

Social and natural events Political turmoil Checklist Question 11(a)

Share market crisis Checklist Question 11(b)

Natural disaster Checklist Question 11(c)

Technology

Acceptability

Innovation Characteristics

Ease of use 5 point semantic

scale

Question 14(a)

Convenience 5 point semantic

scale

Question 14(b)

Complexity 5 point semantic

scale

Question 15

Cost 5 point semantic

scale

Question 14(e)

Security 5 point semantic

scale

Question 14(d)

Time consumption 5 point semantic

scale

Question 13

Bridge of familiarity Incorporation with prior media 5 point semantic

scale

Question 14(c)

Familiarity with technology metaphor Yes/No Question 1

63

ANNEX 7: QUESTIONNAIRE 10.7

Survey on Consumer Adoption of M-Banking in Urban Region of Bangladesh

Dear Sir/Madam,

We are students of Institute of Business Administration, DU. As per the requirements of our

Research Methodology course, we are conducting a research on “Mobile Banking in Urban

Bangladesh: Adoption scenario, Market trends and Future prospects”. The information

provided here will be kept confidential and strictly used for academic purposes only. We will

appreciate your time and cooperation in completing the survey questionnaire.

Identification details:

Respondent Name:

Contact Number :

Address:

Date & Time :

To be filled in by the survey conductor

Mode of conduction: Respondent ID: 3

A. Please tick a single option for the each of the questions provided below:

1. Are you familiar with the term m-banking? [ If yes, go straight to question

number 5 in page 2]

a. Yes b. No

2. Would you be interested in m-banking? (Mobile Banking is a financial transaction

conducted by logging on to a bank's website using a cell phone, such as viewing

account balances, making transfers between accounts, or paying bills.)

a. Yes b. No

3. What problems of the existing banking process would you expect to be solved?

a. Time consumption b. Cost

c. User friendliness d. Security

4. What activities do you perform in mobile handset?

a. I can just make a phone call

b. I can make calls, message my „FnFs‟ and also use some other basic

activities

64

c. I can operate all the functionalities/apps of a phone

B. Please select and put a tick mark in the box for each of the questions given

below:

5. Do you use mobile –banking as one of the main ways of handling your bank

transactions?

Yes No

6. How frequently do you use computer in different areas of your life?

Usage areas

Very Frequent

Use (3 or more

times in a day)

Frequent Use

(Once or

twice in a

day)

Indifferent

(Once or

twice in a

week)

Irregular Use

(Once or twice

in a month)

Can’t

Use

Professional Life

Personal life

7. How frequently do you use internet in different areas of your life?

Usage areas Very

Frequent

Use

Frequent Use Indifferent

About It

Irregular Use

Don’t

Use

Professional Life

Personal life

8. Do you use social networking websites?

Yes No

9. Influence of social networking sites on you:

Unaffected by the views and trends 1 2 3 4 5 High influence

10. How much do you rely on your peers and family members when adopting new

technologies?

Totally rely on

them

Rely on them Somewhat rely

on them

Very little rely on them Don‟t rely on them at

all

11. Influence of external factors on m-banking usage: (If you are not a mobile banking user at

the moment, then please move to part D for your answers)

Decreases Remains the same as usual Increases

During political turmoil my usage of m-banking

During share market crisis my usage of m-banking

During natural disasters my usage of m-banking

C. Please provide your rating in the following box with a tick mark (Rating value

increases with increase of numeric value)

65

13. Level of time consumption on different banking transactions:

Low 1 2 3 4 5 Very high

Off line banking

M-banking

On line banking transactions

Courier Service

14. Your perception on m-banking in general:

1 2 3 4 5

Difficult technology Easy to use

Inconvenient Convenient

Different than e-banking Similar technology

Risky Safe

Costly Affordable

15. Learning and adopting the m-banking procedure is:

1 2 3 4 5

Simple Complex

. D. Personal Data (Please provide information in the space below):

i. Gender: I ii. Age: year iii. Educational qualification:

iv. Profession:

v. What is your monthly income range (BDT)?

a. Below 15000 c. 15000-50000

b. 50000-100000 d. Above 100000

vi. What is your average monetary transaction in a month?

a.Below 5000 b. BDT 5,000- BDT 15,000.

c. BDT 15,000-BDT 30,000. d. BDT 30,000-BDT 50,000.

e.BDT 50, 000 or more.

1 | P a g e

ANNEX 8: KII FINDINGS 10.8

1. Name of Interviewee : Mr. Mahbub Jeshan

2. Organization – bKash limited ( A BRAC Bank Company )

3. Designation – Manager, Trade Marketing , Sales and Distribution

4. Contact- 01720812527

Introduction to A New System :

To be straight forward, bKash is growing rapidly in Bangladesh, a country with 95 million

mobile phones as an unique medium for money transactions and deposit which will not

develop in developed countries in near future. Fundamentally bKash was designed for the

rural people to assure greater financial inclusion in their life. BKash oversees a network of

60,000 agents across the country who let people connect to its service like mom-and-pop

ATMs. In this way it gets around the overhead expenses that a traditional bank would have to

pay.

“So, a merchant who lives far from a bank can use his or her mobile phone to send virtual

money and go to a local agent to receive money. “- Simple and Convenient for any person

Other than its technical perfection , bKash ensures four core values to the consumer of bKash

M-Banking. They are- 1. Fast Service 2. Affordability 3. Security 4. Convenience. Even

bKash has options for foreign remittance transfer with the lowest fees/ service charge. bKash

has diversified in adding many values to its 60,000 agents in the nation.

Mobile Banking Adoption Scenario:

In rural market there is lack of adequate bank branches as well as lack of education/

awareness of mass people regarding accessing the formal banking channel. They find it

complicated & bureaucratic to avail traditional banking service. On the hand “M-Banking”

process is learned through the Word of Mouth, Popularity among referent people and opinion

leaders . These actors in rural society channel the advises , functionality and trust to others.

If we look into the city, we can also find its adoption scenario getting brighter everyday but

its initial TG was not urban population. As a result, there was less focus to aggressive

push/pull for bKash Limited. Nonetheless, urban population seems brighter with the value of

convenience and fast Service to people of all ages and career. Specially, the youth and the

SMEs might show a brilliant scenario of adopting bKash services in future.

Factors Contributing to the adoption process of M-Banking :

We can begin with the lack of availability of formal banking service all over the country

with most pressing reason for m-banking to flourish . As there is no single entity who

has presence in all 64 districts & in all 600 Thanas , there was always a mighty market

need to financial service which reaches the widest population. Here came the scenario of

2

mobile banking , where mobile penetration is in every level of upzilla, unions and all the

600 thanas.

Another contributing factor goes back to the lack of proper education of low income &

lower-middle class people to avail formal banking service. They are yet to get connected

with the system of traditional banking. And now with the presence of mobile banking, it

is tough for traditional banking services to make their presence as widely as bKash or m-

banking has made till now.

Then next reason is the high mobile penetration rate. As per Bangladesh Bank data only

16% Bangladeshi has bank account whereas mobile penetration rate is 60% . People

found it comfortable to do transactions through mobile than bank branches. As mobile

phone becomes a part of daily life hence majority people can adopt MFS operating

procedure very easily.

Lastly, but most importantly, flexibility and technological superiority of mobile financial

services is the factor that will continue to positively influence the m-banking scenario

with time and generations . Such an example of flexibility can be - Traditional Banking

services are only available up to 10 AM – 4 PM, 5 working days only. Whereas mobile

financial services can be availed 24 hours 7 days a week (24/7) all over the country. This

also is easy, secured, fast, and instant.

Obstacles for bKash or m-banking situation of Bangladesh :

One thing that comes with any product or service is proper awareness of its functionality and

presence. Consequently, lack of awareness of customers/ potential customers is the greatest

hurdled face for m-banking to increase its market share or the whole market. Non-

compliance issues are also major issue for urban population . The traditional banking services

are well established and aggressive in terms of promoting themselves. Amidst that, a proper

gourd, joint ventures and collaborative effort is needed to enhance the field of m-banking.If

anyone comes to join any bank which is providing the m-banking services they will be able to

understand about the unhealthy competition among companies and their policies. This is

discouraging new-entrants as well as present ones to diversify. Currently our and any bank‟s

greatest fear is fraudulent activities which is another obstacle which prevails from the

initiation of banking . If I need to give one solution than it would be increasing the market ,

which basically means increasing consumers of all kind under this umbrella as well as

increasing the collaborative effort to establish m-banking as regular banking method in

Bangladesh.

Market Competition and bKash :

Every organization is trying to compete with their own key strengths. Everyone is trying to

set up their sales & distribution channel, technology platform and partnership with mobile

network operators. As this is actually the foundation level for all mobile financial services. If

they are successful in it they need a great supple chain to include every district in

Bangladesh. Some of the new competitors or potential competitors are actually thinking of

niche market too. For now bKahs has created strong distribution Channel, state of the art

3

technology (partnership with Visa) and partnership with all major mobile network operators

to generate smooth system within the realm of m-banking.

Essential Checklist for M-banking Improvement:

Firstly, the idea of “e-money” should more wide spread in our country. This will be a

gateway to build trust upon the m-banking system. Not only that, the concept is also new

which says that more trial based activity or opportunity should be given to the consumers

before they switch to m-banking. The next important step to help m-banking is to develop the

whole eco-system of “e-money” as a alternative of physical cash. Unless or until banks and

financial institutes do that, we won‟t be able to see a sustainable m-banking system that

encompasses the majority. The first step for realizing any goal for now would need us( the m-

banking providers ) to introduce more value added services like “e-ticketing,”, “mobile top

up” , “foreign remittance disbursement” . They will be the extension of our core services. It

will primarily hold the initial market as well as build enough faith in others to bring them

under the blessing of Mobile Banking.

Adoption trend for mobile banking in Urban Bangladesh

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