Conumer Adoption of Internet Banking.txt

CONSUMER ADOPTION OF INTERNET BANKING

Abstract

The rapid changes in Malaysian banking system have created a new dimension

in the banking industry with the emergence of Internet banking. The study also

explores the consumer’s adoption behaviour driven by the evolution of new,

banking technology in Malaysia. The result implies that a social norm effect

dominates Internet banking adoption

This paper presents the profile of the internet banking adopter in Malaysia based

on a large scale survey. A logistic model is used to estimate the probability of a

bank customer adopting internet banking. The profile of the adopter is

constructed using demographic, social economic and technological capacity

indicators. The method is a very simple one, which can be used by financial

institutions to gain better understanding of their own internet banking customers.

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Introduction

Banks have traditionally been in the forefront of exploiting technology to improve

their products, services and efficiency. The banking industry has seen many

technological changes in the last forty (40) years, which has shaped it from

manually intensive industry into one that is highly automated and technologically

dependent.

Internet banking is both a process and product electronic innovation (Chang

2004). It enables customers to handle their banking transactions online, without

physical visits to the bank. In general, however, new innovations particularly

technological ones, are not readily accepted and adopted by everyone. Rogers

(1995) classifies innovation adopters into five broad categories:

(1) innovators,

(2) early adopters,

(3) early majority,

(4) late majority, and

(5) laggards.

Innovators are the first adopters, who are interested in technology itself and

possess positive technology attitudes. On the other hand, early adopters are also

interested in technology and are willing to take risks. The International Data

Corporation investigated urban internet users in six Asian countries (IDC 2002)

and found some important profiles of internet adopters. Early adopters of

wireless internet were usually young and male tech-savvy users at an average

age of 28 years. 64% of them were male. In contrast, the early majority category

consisted of mainly young working adults. There was a larger proportion of

females and working population within the late majority category. Finally,

laggards were found to be predominantly older people.

In Malaysia, a large scale survey was conducted in 2004 to examine selected

individual characteristics of over 800 bank customers. This paper reports some

major findings of the Malaysian study. Using data obtained from the sample, an

attempt is made in this paper to construct the profile of a 'typical' customer who

has adopted internet banking, and to estimate marginal effects of general

individual attributes on the predicted probability of the internet banking adopter.

Methodology

A large scale survey was conducted on the island of Penang in Malaysia. The

subjects consisted of bank customers outside of bank branches and shoppers

who maintained bank accounts. Out of the total of 831 questionnaires collected,

763 were used for the purpose of data entry, processing and analysis. Among

others, the study attempted to explain the probability that a person will adopt

internet banking. For this purpose, the explanatory variables were confined to

individual attributes, which are divided into three categories, viz., demographic,

social economic standing, and technological competency/access. Theses factors

are then examined to see how they affect the dependent variable, which is the

adoption of internet banking. A logit specification is adopted; the parameters in

the discreet choice models are transformed to yield estimates of the marginal

effects, i.e. the change in predicted probability associated with changes in the

explanatory variables. The model reported in this paper is therefore a very simple

one, aiming only to provide an initial estimate of the probability of a person

adopting internet banking, and explain how marginal changes in each

explanatory variable affect the probability of adoption.

The logit model is written as follows:

PPLog-1

= ß1+ ß2X2+ ß3X3+ß4X4+ß5X5+ß6X6+ß7X7+ß8X8+

ß9X9+ß10X10+ß11X11+ß12X12+ß13X13+e

Where: P = the probability of respondents adopting

Internet banking facilities

Dependent variable: Y = 1 if adopt Internet banking facilities

0 otherwise (preferring other alternatives);

Explanatory variables: X = hypothesized to influence probabilities of

independent variables

ß = coefficients of the explanatory variables;

e = stochastic disturbance term.

Profile of the 'typical' Internet Banking Adopter

Table 1 shows the mean values of the explanatory variables as found in the

survey sample (column 2). Using the mean values, we can construct the profile

of a 'typical internet banking adopter.'

In order to calculate the marginal effects of probability, the mean values of age,

income and frequency of visits obtained from the sample date to be calculated

with the probability of the “typical: respondent. Substituting these values for the

typical respondent into the estimated logit equation yields the log of odds of

adopting Internet banking to be 0.78361. The probability of adopting Internet

bank is computed as 0.69.

The survey shows that a 'typical' Malaysian internet banking adopter is a

Chinese male aged 29. He has had tertiary education, works in the private sector

or is self-employed, drawing a monthly income of RM2255. The typical internet

banking adopter is married and resides in urban areas. Proficient in the English

language which is the most common medium of communication in the internet;

he is also computer literate and has ready access to the internet. In general, he

visits the bank three times a month and is a risk averter.

Table 1: Profile & Marginal Effects on the Probability of Adopting Internet

Banking

Independent Variables

Sample

Marginal Effects

Ethnicity

Chinese

0.08007

Gender

Male

0.05818

Age

29

-0.00597

Education

Tertiary

0.11856

Occupation

Private Sector/self

employed

0.11654

Income (Malaysian ringgit)

2255

0.00019

Marital Status

Married

-0.04024

Residence

Urban

0.08769

Language

English

0.11611

Frequency

3

-0.20230

Risk

Risk Averter

0.10001

Computer Literacy

Computer Literacy

0.23964

Internet Access

Internet Access

0.27436

Marginal Effects on Probability of Adopting Internet Banking

A significant advantage of using the logit model is the opportunity provided by the

model for us to examine the marginal effects. The estimated logit coefficients

generated from our sample data show how individual characteristics affect the

log odds of adopting internet banking. Given the estimated coefficients and the

changes in odds, we can compute, using the binary choice logit model, the

predicted probability of the adopter for any change in any of the explanatory

variable. The marginal effects shown in the last column of Table 1, which are

associated with respective explanatory variables, have been computed from the

estimated coefficients of and the changes in the log of odds.

Table 2. Marginal Effects on the Odds of adopting Internet Banking

Independent

Variables

Estimated

Coefficient

Standard

Error

Z-Statistic

Probability

Value

Constant

-2.716598

0.730506

-3.718789

0.0002

Race

0.374360

0.219628

1.704517

0.0883*

Gender

0.271976

0.215566

1.261684

0.2071

Age

-0.027898

0.016668

-1.673692

0.0942*

Education

0.554293

0.216965

2.554758

0.0106**

Occupation

0.544829

0.219383

2.483460

0.0130**

Income

0.000907

0.000118

7.663284

0.0000***

Marital

-0.188123

0.216540

-

0.868770

0.3850

Residence

0.409939

0.237924

1.722981

0.0849*

Language

0.542811

0.217844

2.491745

0.0127**

Frequency

-0.945750

0.126295

-7.488403

0.0000***

Risk

0.467567

0.212675

2.198504

0.0279**

Comp Literacy

1.120358

0.227604

4.922409

0.0000***

Internet Access

1.282649

0.242372

5.292064

0.0000***

Likelihood ratio

(13df)

370.3896

Probability (LR

Stat)

0.000000

*** Significant at 1 percent level of significance

** Significant at 5 percent level of significance

* Significant at 10 percent level of significance

Adoption of Internet banking is expected to be positively related to race, gender,

education, occupation, income, residential area, language, risk, computer literacy

and Internet access but negatively related to age, marital status and frequent visit

to the bank branch. All the variables have the expected signs, although not all the

variables are statically significant.

Table 3. Marginal Effects on the Odds of Adopting Internet Banking

Independent Variables

Estimated Coefficient

ß

Change in Odds

.ß

Race

0.374360*

1.4541

Gender

0.271976

1.3126

Age

-0.027898*

0.9725

Education

0.554293*

1.7407

Occupation

0.544829*

1.7243

Income

0.000907*

1.0009

Marital Status

-0.188123

0.8285

Residence

0.409939*

1.5067

Language

0.542811*

1.7208

Frequency

-0.945750*

0.3884

Risk

0.467567*

1.5961

Comp Literacy

1.120358*

3.0660

Internet

1.282649*

3.6062

* Statistically significant variables at various levels

Income of the respondents is positively correlated with adoption of Internet

banking. As income increases the tendency to adopt, Internet banking also

increases. When income increases by RM1.00, the log of odds of adopting

Internet banking increases by 0.0009. Higher income households tend to react

more positively by adopting Internet banking because they come from the more

affluent part of the society.

The log of odds for respondents who are frequent visitor to the bank

branches, adopting Internet baking is 0.9458 times lower than respondents who

are not frequent visitors to bank branches. Ceteris paribus, when the number of

visits to the bank branches increases by 1 time, the odds of adopting Internet

banking decreases by 61.2%.

The relationship between the decision to adopt Internet banking and

computer literacy is statistically significant at 0.01 level of significance. The result

suggests that the log of odds for computer literate respondents adopting Internet

banking is 1.1204 times higher than respondents who are computer illiterate.

Those who are computer illiterate significantly have a lower likelihood to adopt

Internet banking. This result also suggest that, ceteris paribus, the odds of

adopting Internet banking increases by 3.0060 times when a respondent is

computer literate compared to one who is computer illiterate.

The relationship between decision to adopt Internet banking and Internet

access is one of the most important variables. Internet access is positively

related with the likelihood to adopt Internet banking, at 0.01 level of significance.

The log of odds for respondents having Internet access adopting Internet banking

is 1.2826 times higher than respondents without Internet access. The findings

indicate that, ceteris paribus, the odds of adopting Internet banking increase by

3.6062 when a respondent has Internet access.

The log of odds of a tertiary educated respondent adopting Internet

banking increases by 0.5543 compared to non-tertiary education. This study also

indicates that, other things being equal the odds of adopting Internet banking

increases by 74% when a respondent has tertiary education compared to those

who do not possess tertiary education.

Occupation is positively and statistically significant related to adopting

Internet banking. The relationship between occupation and adoption of Internet

banking is statistically significant at 0.05. The log of odds for private sector or

self-employed respondent adopting Internet banking is 0.5448 times higher than

public sector respondents. The outcome also indicates that, ceteris paribus, the

odds of adopting Internet banking increases by 72% when a respondent is a

private sector employee or self-employed compared to public sector employee.

The other significant difference appears to be in the average level of monthly

income in the private sector or self employed is higher compared to public

sectors.

The impact of selected marginal effects on the predicted probability of the typical

internet banking adopter is shown in Table 4.

To start with, Case no. 1 in Table 4 represents the profiles of the 'typical internet

banking adopter' discussed earlier, which has been constructed from the mean

values of the variables. The predicted probability of 0.69 simply means that bank

customers with such individual profiles have a 69% chance of being an internet

banking adopter. However, what happens to the predicted probability if the profile

of the typical adopter is changed marginally each time? Using data from Table 2,

we shall look at marginal effects arising from demographic and social economic

factors below.

Marginal effects of Demographic Factors

Suppose the bank customer happens to be a non-Chinese, but all other

characteristics of the typical adopter remain the same? Case no. 2 shows that

the predicted probability diminishes by 0.08 compared to the original typical

adopter. As a result, the predicted probability of such a person is now 0.60. This

means that a non-Chinese customer identical in all other attributes will be 8%

less likely to adopt internet banking. The next two cases indicate the changes in

the predicted probability consequent to marginal changes in other demographic

factors (viz. gender and age). It shows that an older person, ceteris paribus, is

less likely to adopt internet banking. If the age of the typical adopter is increased

to 45, (case no. 4), the predicted probability of internet banking adoption

diminishes considerably to 0.58.

Table 4: Marginal Effects on Probability of Adopting Internet Banking

No

Characteristics

Predicted

Probability

1

Chinese male age 29 with tertiary education, working in

private sector/ self employed, monthly individual income of

RM2255, married, residing in urban area, ability to

communicate in English language, average of visiting bank 3

times in a month, risk averter, literacy in computer and have

access to internet

0.69

2

Non-Chinese male, age 29 with tertiary education, working

in private sector/ self employed, monthly income of RM2255,

married, residing in urban area, ability to communicate in

English language, average of visiting bank 3 times in a

month, risk averter, literacy in computer and have access to

internet.

0.60

3

Chinese female, age 29 with tertiary education, working in

private sector/ self employed, monthly income of RM2255,




internet.

0.63

4

Chinese male, age 45 with tertiary education, working in





internet.

0.58

5

Chinese male, age 29 with non tertiary education, working

in private sector/ self employed, monthly income of RM2255,




internet.

0.56

6

Chinese male, age 29 non tertiary education, working in





internet.

0.41

7





0.81


internet.

8



married, residing in rural area, ability to communicate in



internet.

0.59

9





month, risk averter, non literate in computer and have

access to internet.

0.42

10





month, risk averter, literacy in computer and have no access

to internet.

0.38

We can similarly estimate marginal effects of social economic factors on the

predicted probability of the typical adopter. Cases 5 to 8 in Table 2 show some

worked out examples. Income appears to be a major determinant of adoption. A

slight increase in the person's monthly pay to RM3000 enhances the probability

from 0.69 to 0.81 (Case 7). On the other hand, if the typical person has only

RM1000 in monthly income, the predicted probability declines to 0.41 (Case 6)!

Residence can be classified as either demographic or social economic factor.

Table 2 shows that residing in rural areas reduces the predicted probability of

adopting internet banking to 0.59. Among social economic environmental

limitations, non-availability of internet services in the rural areas is definitely a

significant constraint, reducing the predicted to a mere 0.38 (Case 10).

Conclusion

This short paper attempts to show that using the logistic model, it is possible to

quickly come up with a profile of bank customers who adopt internet banking.

Most of the data on the explanatory variables, such as the demographic and

social economic indicators can be readily found in the data base of customers.

Hence financial institutions will be able to construct the profile of their own

customers. Knowledge of the profiles or more generally the individual

characteristics of customers is useful in many ways. Equipped with information

on customers who already adopt internet banking or likely to do so, banks will be

able to identify the market segments that should be targeted. They can then

introduce banking products and services that better suit the needs and wants of

the customers in the segment.

References

Abdullah, Z. (1985), A critical review of the impact of ATMs in Malaysia. Bankers

Journal Malaysia. April 1985. pp. 13-16.

Chang, Y.T. (2004), Dynamics of Banking Technology Adoption: An Application

to Internet Banking. http://www.uea.ac.uk/~j106/IB.pdf, accessed 8 Jan. 2005.

IDC (2002), IDC predicts youths and road warriors to lead wireless data

adoption. http://www.idc.com.sg/Press/2002/AP-PR-unwiring.html, accessed 19

March 2002.

Moore, C. and Benbasat, I. (1991), ``Development of an instrument to measure

the perceptions of adopting an information technology innovation", Information

Systems Research, Vol. 2 No. 3, pp. 192-222.

Rogers, E. (1995), Diffusion of Innovations , 4th ed., The Free Press, New York,

NY.

Sathye, M. (1999), "Adoption of Internet banking by Australian consumers: an

empirical investigation", International Journal of Bank Marketing, Vol. 17 No. 7,

pp. 324-34.

Teo, T. (2001), "Demographic and motivation variables associated with Internet

usage activities" Internet Research: Electronic Networking Applications and

Policy, Vol. 11 No. 2, pp. 125-37.

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Conumer Adoption of Internet Banking.txt