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Int. J. Mobile Communications, Vol. 5, No. 2, 2007

An overview of mobile banking adoption among the urban community Ainin Sulaiman*, Noor Ismawati Jaafar and Suhana Mohezar Department of Marketing and Information Systems, University of Malaya, 50653 Kuala Lumpur, Malaysia E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author Abstract: Technological advances have changed the way in which financial services are transacted, with mobile banking being the latest development in this domain. This paper focuses on the adoption of mobile banking services by consumers. Rogers’ diffusion of innovation model was adopted to study the consumers’ behaviour and motivation towards this innovation. The personal characteristics of mobile banking users were found to be important determinants of their adoption decisions. This finding provides the financial services industry with a better understanding of customer perceptions of mobile banking services and helps them plan their marketing strategies and promotion approaches for mobile banking services in the future. Keywords: mobile banking; financial services; consumer behaviour; diffusion innovation; mobile commerce. Reference to this paper should be made as follows: Ainin, S., Noor Ismawati, J. and Mohezar, S. (2007) ‘An overview of mobile banking adoption among the urban community’, Int. J. Mobile Communications, Vol. 5, No. 2, pp.157–168. Biographical notes: Ainin Sulaiman is currently serving as the Director of the Graduate School of Business, Faculty of Business and Accountancy, University of Malaya. Her areas of expertise are information technology management, e-commerce and management information systems. She is actively involved in research on e-commerce, digital divide and mobile commerce. Noor Ismawati Jaafar is currently serving as a Lecturer of Accounting Information Systems in the Department of Marketing and Information Systems in the University of Malaya’s Faculty of Business and Accountancy in Kuala Lumpur, Malaysia. Her interests are in the areas of knowledge management, IT governance and information systems. Suhana Mohezar is currently serving as a Tutor in the Department of Marketing and Information Systems in the Faculty of Business and Accountancy, University of Malaya. Her research interests include supply chain management, e-commerce, and m-commerce.

Copyright © 2007 Inderscience Enterprises Ltd.

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Introduction

The development of new technologies has profoundly changed the way in which customers interact with service providers. Traditionally, the distribution of services in the retail banking industry largely meant customers having to visit a physical branch (‘bricks and mortar’) to access any financial services (Smith, 2006). Today, the situation has changed. According to Laukkanen (2006) the opportunity to avoid queuing at the Automated Teller Machine (ATM) was perceived an important factor in electronic banking. Technology has become an increasingly vital element in the competitive landscape of the financial service industry. According to AlShaali and Varshney (2005), the improved network bandwidth and wireless application technologies have created opportunities for wider deployment and usage of mobile commerce services. Smith (2006), in his paper, mentioned that the adoption of online banking services has been ranked as one of the fastest growing activities in the year 2003. The technological deployment in financial services has changed the nature of selling and buying financial services (Suoranta and Matilla, 2003). Mobile banking, where customers access bank services remotely by using mobile devices with wireless connectivity, is one of the latest service delivery modes available from banks. Customers can conveniently check their account balances and transaction histories, transfer funds, pay bills, trade stocks and obtain stock quotes, manage their investment portfolio, and order insurance using mobile devices (Durkin et al., 2003). However, even though the technology has been available for some time, financial institutions in most developed countries have only recently begun to offer mobile banking to their customers, and where it is available, its usage has been fairly limited. According to Choon et al. (2004), the overall level of the current B2C mobile business model is unsatisfactory because customers are not familiar with the mobile commerce environment. This paper adopts Rogers’ (1995) research on ‘diffusion of innovations’ model. Rogers (1995) recognised five categories of consumers who differed in terms of innovativeness, which influenced the adoption of technological innovations. These categories were: innovators, early adopters, the early majority, the late majority and the laggards. This paper applies Rogers’ work to the domain of mobile banking by seeking to identify the typical characteristics of mobile banking users, including their personal innovativeness, compared to non-mobile banking users. The objective of this research is to identify the characteristics of typical mobile banking users and to determine the level of innovativeness of the adopters of mobile banking as compared to adopters of non-mobile banking. In addition, this study also attempts to determine the factors that influence the adoption of mobile banking among the urban community using diffusion innovation theory. The paper begins by reviewing the relevant literature to provide the theoretical background of the study. Thereafter, the methodology is discussed, followed by a description of the findings. The paper concludes with a discussion of the implication of the findings and identifies areas for future research.

2

Literature review

The theoretical framework for this study is based on the ‘innovation diffusion’ model by Rogers (1995). Rogers (1995) defines diffusion as the adoption of an innovation

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‘over time’ by the given social system as a consequence of diffusion processes, which result in the acceptance or penetration of a new idea, behaviour, or physical innovation. Prior studies have traditionally analysed consumers’ adoption of innovation using Rogers’ (1995) five categories of adopters: innovators, early adopters, early majority, late majority and laggards.

2.1 Personal Innovativeness and innovation diffusion Personal innovativeness is found to be the best predictor of technology adoption which is often used in previous research (Agarwal and Prasad, 1998; Lockett and Littler, 1997). Personal innovativeness embodies the risk-taking propensity that is higher in certain individuals. In innovation diffusion research, it has long been recognised that highly innovative individuals are active information-seekers of new ideas. They are able to cope with high levels of uncertainty, and develop more positive intentions towards acceptance (Rogers, 1995). Agarwal and Prasad (1998) defined personal innovativeness in the domain of information technology as “the willingness of an individual to try out any new information technology”. They postulated that individuals with a higher level of innovativeness with respect to information technology would be expected to develop more positive perceptions about an innovation in terms of its advantages, ease of use, and compatibility. Hence, they would be more inclined to adopt new technology. Different categories of adopters may differ in terms of their preferences for varying information sources as well as their propensity to rely on information provided by marketing departments. In this instance, individuals will vary in the amount of trust they place on information provided by marketing departments, independent third parties and personal sources. The distinction between Rogers’ five categories of adopters is based on innovativeness, and suggests that new products and services should be targeted at innovators, who start the diffusion process by communicating to other adopter segments (Black et al., 2001). ‘Innovators’ are the first adopters (Saaksjarvi, 2003), and they tend to be interested in the technology itself, possessing positive attitudes towards technology (Mohr, 2001). Innovators tend to be heavier users of professional communication sources such as sales people and governments. These consumers are seen as leaders and technology pioneers. They recognise the benefits of new technology earlier than others, adopt it, and communicate these benefits to other adopter segments. They are willing to test and reduce errors in innovative products just to get access to the latest technologies. According to Parasuraman and Colby (2001), access to the new technologies provides innovators with mental stimulation. Rogers (1995) stated that the prerequisites of innovators include having substantial financial resources to absorb possible losses from an unprofitable innovation, the ability to understand and apply complex technical knowledge, and the ability to cope with a high degree of uncertainty. An innovator’s position exposes him to technological innovations more than other segments of adopters. Exposure is important to innovators who are interested in technology for its own sake and enjoy examining technological innovations. According to Saaksjarvi (2003), innovators possess a greater degree of innovativeness, followed by the early adopters, the early majority and laggards. Hence, innovativeness is closely related to consumers’ willingness to learn about new products and have positive attitudes towards new products, which serves as a basis for adopting the technology.

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2.2 Demographic profile and innovation diffusion In terms of the effects of demographics on innovation diffusion, Venkatesh and Morris (2000) recently investigated gender differences in the context of individual adoption and sustained usage. They found gender to be an important determinant of technology adoption and usage. According to the Target Group Index (Target Group Index Europe Survey, 2000), there were markedly more men in Germany’s market, which had the lowest level of mobile phone ownership in Europe (60% men vs. 40% women). In the same vein, a recent survey conducted in the UK found that men spend more time on mobile phones than women (NST, 2002). In contrast, Wan et al. (2005) found that gender has the least useful dimension for market segmentations, as there were only slight differences between males and females in internet banking adoption. DeBaillon and Rockwell (2005), in their study, revealed that the gender gap in cellular telephone use is narrowing, with the man and woman reporting virtually equal usage. Besides gender, Venkatesh and Morris (2000) also suggested that gaining a better understanding of age differences is important, particularly as it relates to user acceptance and usage of new information technologies. Early adopters of new products are commonly thought to be young in most technology markets. According to Polatoglu and Ekin (2001), demographic factors that describe electronic banking services adopters include the young, the affluent and the highly educated. Similarly, a Finnish study (Matilla et al., 2003) reported that the internet banking user is middle aged, relatively wealthy and highly educated.

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Research methodology

A research model is developed to illustrate the relationship between the independent variables and the dependent variable. With reference to the literature review (Rogers, 1995; Venkatesh and Morris, 2000; Polatoglu and Ekin, 2001), the following research model was developed (Figure 1). Figure 1

Research model

Primary data were collected using a questionnaire with two screening question; “do they have a bank account” and “do they use the internet”. The questionnaire was divided into two sections. Section A focused on collecting the respondent’s demographic details, such as gender, age and personal income. Section B was used to determine the

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respondents’ characteristics and to determine the level of innovativeness. The items used to determine the level of innovativeness of respondents were adapted from Rogers (1995) and Lockett and Littler (1997). A 5-point Likert scale was used for all items. Respondents indicated their level of agreement with carefully constructed statements that ranged from positive to very negative toward the attitudinal object. In the interests of expediency and practicality, a convenience sampling method was used to obtain respondents. The limitation of this method is that we are unable to ascertain whether the information collected is representative of the population as a whole. We will bear this limitation in mind when we report the results. The target respondents were working adults, aged between 18 to 55 years, who were both bank customers as well as internet users. The questionnaire was self-administered and was distributed to 300 randomly selected respondents located around the Klang Valley, an area within the capital city of Malaysia, Kuala Lumpur. This area is chosen as the area which has the highest internet penetration as well as internet users in Malaysia (Eight Malaysia Plan 2001–2005). The drop-off method was used; that is, the questionnaires were distributed and collected after half an hour. Two hundred seventy nine complete responses were received.

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Data analysis and results

4.1 Profile of respondents Approximately 54% of the respondents were male, while 45.9% of the respondents were females. Previous research has shown that males outnumbered females in internet usage. Hence the results of this study can be used to make a generalisation. The respondents can be classified into three different groups, based on their educational background. Fifty seven percent of the respondents currently possess a degree, 18.6% possess a Master’s degree, while 24.4% have a secondary school education. In terms of monthly income, out of 279 respondents, 1.1% earn below RM1,500, 46.2% earn between RM1,500 to RM3,000,42.3% earn between 3000 to RM5,000 and 10.4% earn between RM5,000 to RM7,000. The respondents can be classified into three segments based on their age. Those between 21 and 30 years old formed the highest percentage (50.9%), while respondents between 41 and 50 years old were the least (6.5%). Approximately 50% of the respondents were between 31 and 40 years old.

4.2 Characteristics of mobile banking users The characteristics of mobile banking users were identified by examining the relationship between the respondents’ demographic profiles and whether they adopted mobile banking. The results of the study are elaborated in the following paragraph. It was found that out of 279 respondents, 149 (53.4%) have adopted mobile banking. The relationship between mobile banking adoption and gender is shown in Table 1. Approximately 70% of males use mobile banking; in comparison, only 34.4% of the females did so. This is consistent with the previous research, which found that males are more likely to adopt technological innovation than females (Venkatesh and Morris, 2000).

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Table 1

Relationship between mobile banking adoption and gender Adopt mobile banking

Gender

Do not adopt mobile banking

Total

Frequency

Percentage

Frequency

Percentage

Frequency

Percentage

105

69.5

46

30.5

151

100

44

34.4

84

65.6

128

100

Male Female

Mobile banking is most popular among respondents from 21 to 30 years old group (Table 2). In contrast, only 5.6% of the respondents between 41 and 50 years old adopt mobile banking. This shows that mobile banking is more popular among younger consumers. Table 2 Age category (years)

Relationship between mobile banking adoption and age Adopt mobile banking

Do not adopt mobile banking

Frequency

Frequency

Percentage

21–30

88

62

31–40

60

50.4

41–50

1

5.6

54

Percentage

Total Frequency Percentage

38

142

100

59

49.6

119

100

17

94.4

18

100

Table 3 represents the relationship between mobile banking adoption and education level. Only 2.9% of the respondents, who have secondary education and below, adopted mobile banking. In comparison, 75% of the respondents who possessed a Master’s degree used mobile banking. The result shows that mobile banking adopters are, typically, customers who have high educational background. This is consistent with the previous research by Polatoglu and Ekin (2001). Table 3 Education level Secondary and below First degree Master’s degree

Relationship between mobile banking adoption and education level Adopt mobile banking Frequency Percentage

Do not adopt mobile banking Frequency

Percentage

Total Frequency Percentage

2

2.9

66

97.1

68

100

108

67.9

51

32.1

159

100

39

75.0

13

25

52

100

Table 4 displays the relationship between income level and mobile banking adoption. None of the respondents who earned below RM1,500 monthly adopted mobile banking, while 89.7% of the respondents who earned between RM5,000 to RM7,000 monthly did so. Hence, mobile banking adoption is prevalent among high-income earners, consistent with Matilla (2001).

An overview of mobile banking adoption among the urban community Table 4

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Relationship between mobile banking adoption and income level Adopt mobile banking

Income (RM)

Frequency

Below 1,500

Percentage

Do not adopt mobile banking Frequency

0

0

3

1500–3000

45

34.9

84

3000–5000

78

66.1

40

5000–7000

26

89.7

3

Total

Percentage

Frequency

100

Percentage

3

100

65.1

129

100

33.9

118

100

10.3

29

100

The relationship (Table 5) between the adoption of mobile banking and demographic profile (gender, age, education level and income level) was tested using the Pearson’s Chi-square test. All four variables were found to be significantly associated with mobile banking. This implies that there are higher levels of adoption among male, younger people, those with higher income and those who have a higher level of education. Table 5

Relationship between mobile banking adopters and demographic variables

Relationship

Value

Asymp sig.

Significant

Mobile banking and gender

34.419

0.000

Yes

Mobile banking and age

21.176

0.000

Yes

Mobile banking and education level

92.806

0.000

Yes

Mobile banking and income level

44.180

0.000

Yes

4.3 Personal innovativeness and mobile banking adoption Sixteen items were used to measure the level of personal innovativeness among mobile banking adopters. It was observed that mobile banking adopters were more eager to try new ideas, compared to non-mobile banking adopters (Table 6). Mobile banking users are found to be more adventurous (M = 3.99) than non users (M = 2.65) as they would be more inclined to try out any new product (item 6). This implies that mobile banking adopters tend to be people who have pioneering characteristics. Table 6 also illustrates that mobile banking adopters have favourable attitudes towards change, as shown by the higher mean scores (M = 4.12) compared to non-mobile banking users (M = 2.85), to the statement “I like to keep up with technological advances”. Conversely, non-mobile banking adopters are unwilling to adopt new ideas, unless compelled to do so. This is illustrated by their higher scores on items 12, 15 and 16. Mobile banking adopters have a greater exposure to the mass media and often seek out information about innovations. They use commercial media and professional sources more extensively (item 10) compared to non-mobile banking users. Interestingly, mobile banking adopters (M = 4.15) had higher scores on social participation (item 11) than non-mobile banking adopters (M = 2.9). In addition, the average total score of personal innovativeness for mobile banking users was slightly higher (M = 66.1) than that for non-mobile banking users (M = 49.5) (Table 7). This implies that people with a high level of innovativeness tend to adopt mobile banking services. This finding is consistent with Agarwal and Prasad (1998).

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Table 6

Relationship between mobile banking adoption and personal innovativeness Mean scores Adopt mobile banking

Items

Do not adopt mobile banking

Mean differences

I like to be considered as a leader

3.88

3.11

0.77

My friends and neighbours often come to me for advice about new products and innovation

3.85

2.71

1.14

I often seek advice from friends regarding new products or innovations

3.77

3.29

0.48

I like to buy new and different things

4.07

2.85

1.22

I am eager to try new ideas

4.1

2.91

1.19

I am usually among the first to try new products

3.99

2.65

1.34

I have more self-confidence than others

4.01

2.88

1.13

I want to look a little different than others

3.95

2.83

0.12

I like to keep up with technological advances

4.12

2.85

1.22

I often make extensive use of commercial media and professional sources in learning of new products

4.21

2.9

1.31

I am a socially active person

4.15

2.94

1.21

I have old-fashioned tastes and habits

3.77

4.64

0.87

My social status is an important part of my life

4.11

2.75

1.36

It is very important to me to feel that I am a part of a group

4.15

2.98

1.17

I only accept and use new products because of economic necessity and social pressures

4.0

4.49

0.49

I am a person who is ‘sceptical’ about new ideas

4.04

4.48

0.44

Table 7

Total scores of personal innovativeness Mobile banking adoption

Total scores of personal innovativeness

5

N

Mean

Std. deviation

Adopt

149

66.0537

7.81785

Do not adopt

130

49.5077

11.41823

Discussion and conclusion

This paper provides new, interesting insights into the diffusion pattern of mobile banking services. The fundamental question we are asking is “why do people adopt new products?” The literature review described how demographic and psychographic variables affect the adoption of innovations, and our findings are in line with this argument. Specifically, this study found that demographic factors such as age, gender, personal income and educational background, do affect the adoption of mobile banking services.

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In line with previous study done by Black et al. (2001) and Lockett and Littler (1997), this study also found that males are more willing to adopt new technology than females. The results could be attributed to the male’ masculine personality which is often willing to take risks, and is anxious to try out new technological products. Besides this, men also read more technological magazines and have a greater involvement with technological products. Similar to prior studies which showed that younger consumers have a greater tendency to adopt innovations, such as internet banking, this study found that mobile banking is most commonly adopted by consumers aged between 21 to 30 years. In this context, this is understandable since mobile banking requires the use of mobile devices such as a Personal Digital Assistant (PDA) or 3G mobile phones, which are usually purchased by young consumers with more disposable income. The high educational background of mobile banking users is explained by the need to master the complexity of conducting financial transactions via mobile channels (Black et al., 2001). Finally, this study also suggests that personality traits do have an effect on the likelihood of the decision to adopt mobile banking. The dominant idea within the field of innovation research is that individuals can be categorised according to their innovativeness. These categories, namely, innovators, early adopters, early majority, late majority and laggards, have different levels of innovativeness, and this affects their likelihood of adopting mobile banking. Consumer innovativeness can be measured through their attitudes to change, peer influence, communication behaviours and pioneering characteristics. The more innovative the consumer is, the more likely they will adopt mobile banking. This study only focuses on individual differences and on how individuals perceive mobile banking. Other factors such as the type of innovation decision, communication channels, and the nature of the social system are also important in determining the factors that influence the adoption of mobile banking. For example, the type of innovation decision is related to an innovation’s rate of adoption. We suggest that additional research should be conducted to examine these other factors. Diffusion studies have yet to determine the relative contribution of each of the variables. When such enquiry is accomplished, we shall have a much more adequate basis for planning and allocating the inputs going into campaigns designed to speed up adoption of mobile banking. The results of this paper can help the financial services industry to identify the typical users of mobile banking, based on their demographics, and thus point out the right market segment to target. This knowledge affects a range of decisions, such as the amount of resources to allocate to train sales people and the type of advertising campaigns to run. In addition, financial services providers can also use the findings to improve the way in which information about mobile banking is disseminated. Different target markets need different types of advertising campaigns, and a bank’s communication style should be compatible with the information processing styles of potential adopters. Disseminating information through the right mode of communication for different consumer segments is likely to increase each segment’s probability of adopting mobile banking.

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