Factors influencing online banking adoption - Semantic Scholar

Int. J. Internet Marketing and Advertising, Vol. 6, No. 4, 2011

Factors influencing online banking adoption: evidence from the Austrian market Sonja Grabner-Kräuter* Department of Marketing and International Management, University of Klagenfurt, Universitaetsstr. 65-67, A-9020 Klagenfurt, Austria E-mail: [email protected] *Corresponding author

Robert J. Breitenecker Department of Innovation Management and Entrepreneurship, University of Klagenfurt, Universitaetsstr. 65-67, A-9020 Klagenfurt, Austria E-mail: [email protected] Abstract: Many bank customers are still reluctant to conduct their financial transactions online. The aim of this paper is to provide an improved understanding of determinants of online banking adoption in Austria. The authors propose a conceptual model that integrates perceptions of innovation characteristics and individual differences and report an empirical study with 372 bank customers in Austria. Logistic regression is used to analyse the data. The findings confirm the relevance of perceived innovation characteristics to online banking acceptance. Beyond that, the results suggest that internet trust and preference for personal contact are individual difference variables that predict online banking adoption. Keywords: internet banking; online banking; internet marketing; LOGIT model; innovation characteristics; individual differences; personal contact; online banking adoption; online banking acceptance; Austria. Reference to this paper should be made as follows: Grabner-Kräuter, S. and Breitenecker, R.J. (2011) ‘Factors influencing online banking adoption: evidence from the Austrian market’, Int. J. Internet Marketing and Advertising, Vol. 6, No. 4, pp.333–351. Biographical notes: Sonja Grabner-Kräuter is an Associate Professor of Marketing at the University of Klagenfurt, Austria. Her research focus is on business ethics, consumer trust and electronic commerce. She has published in the Journal of Advertising, Journal of Business Ethics, International Journal of Human-Computer Studies, Journal of Product and Brand Management and International Journal of Bank Marketing. Robert J. Breitenecker has studied Mathematics and completed his PhD in Statistics at University of Klagenfurt in 2007. He is an Assistant Professor of Innovation Management and Entrepreneurship at University of Klagenfurt, Austria. His research interests include spatial and multivariate statistical methods, innovation management and entrepreneurship.

Copyright © 2011 Inderscience Enterprises Ltd.

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1

S. Grabner-Kräuter and R.J. Breitenecker

Introduction

In recent years, developments in information technology and the subsequent evolution of online banking have fundamentally changed the ways in which banks implement their business and consumers conduct their everyday banking activities (Al-Somali et al., 2009; Eriksson et al., 2008; Sayar and Wolfe, 2007). Online banking allows customers to conduct a wide range of banking transactions electronically via the bank’s website – anytime and anywhere, faster, and with lower fees compared to using traditional, realworld bank branches. However, despite the continuing increase in the number of internet users and despite all the apparent advantages of online banking for customers, in many countries the growth rate of internet users who adopt online banking has not risen as strongly as expected (White and Nteli, 2004). For example, in Norway and Finland 70%–80% of internet users adopt online banking, in Austria and Germany about 40%, whereas in Greece and Romania less than 10% of the internet users make use of online banking or brokerage (Meyer, 2006). In Austria (and in many other countries) potential users object to conduct their financial transactions online, yet in spite the huge amount of money banks have spent on building user-friendly online banking systems. Obviously, the benefits of online banking do not, in and of themselves, explain why some consumers accept the new technology and others do not (Lassar et al., 2005). This points out the need to further investigate the factors that ultimately determine consumers’ acceptance of online banking. For banks it is important to understand relevant user characteristics and to be able to assess who specifically is adopting and utilising online banking technologies and why. At first sight, it seems that the literature on the acceptance of online banking is already mature and makes up a consistent theoretical body (Hernandez and Mazzon, 2007). Using numerous different theoretical approaches and models several researchers have investigated the factors that impact the decisions of consumers to adopt online banking (for recent reviews see, e.g., Hernandez and Mazzon, 2007; Sayar and Wolfe, 2007). Especially in the information systems literature questions related to user technology acceptance have received wide and intense interest. However, models such as the technology acceptance model (TAM) proposed by Davis (1989) and extensions and modifications of the TAM that have been suggested by many researchers (for an overview see Chau and Lai, 2003) might not adequately consider the potential influence of psychological and situational factors (Dabholkar and Bagozzi, 2002). The same is true for models built on innovation diffusion theory (IDT) frameworks that posit the impact of certain innovation characteristics on the process of innovation adoption. The use of different theoretical models and different methods of analysis make it difficult and ineffective to summarise and compare the results of empirical studies with the aim of deriving recommendations for bank managers to enhance their customers’ willingness to adopt online banking. Hence in line with Hernandez and Mazzon (2007, p.77) it can be concluded that “…, despite the vast number of existing studies, very little is known about the variables that truly determine the adoption of internet banking”. Aside from that, banks clearly need distinct guiding principles for formulating their marketing strategies to increase online banking adoption. Based on relevant, extant research we propose an integrated theoretical model that includes variables from both TAM and IDT frameworks. To improve the model’s predictive power we consider additional individual difference variables such as need for personal contact. We also incorporate different types of trust in our research model,

Factors influencing online banking adoption

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because recent literature on online banking shows that the lacking of trust is considered to be one of the main reasons why consumers are still reluctant to conduct their financial transactions online (Flavián et al., 2006; Grabner-Kräuter and Faullant, 2008; Luarn and Lin, 2005; Mukherjee and Nath, 2003; Rotchanakitumnuai and Speece, 2003). Going beyond the emphasis on attitudes and behavioural intentions in most of the existing online banking studies our study focuses on actual online banking adoption. In order to investigate the influence of selected variables on actual adoption, we collected data from both adopters and non-adopters of online banking. The paper proceeds as follows: below, in the first section, we give a rough and very short overview of several theoretical frameworks that have been used to investigate the adoption of online banking. The subsequent discussion of antecedent variables as predictors of online banking adoption draws from these frameworks and considers additional variables to improve the model’s predictive power (Davis et al., 1989). Next, an empirical model explaining the adoption of online banking is presented, and results of an empirical study with 372 bank customers in Austria (adopters and non-adopters) are discussed. The paper closes with a discussion of the study’s theoretical and managerial implications.

2

Overview of theoretical frameworks to investigate the adoption of online banking

The analysis of factors that impact the decisions of consumers to adopt innovative retail services such as online banking has extensively focused on the issue of user technology acceptance (Hernandez and Mazzon, 2007; Lai and Li, 2005; Ravi et al., 2006; Wang et al., 2003). Several theoretical approaches have been used and many competing models have been developed to investigate the determinants of acceptance and adoption of new information technology. These theoretical approaches have been discussed extensively in the literature (for comprehensive reviews see, e.g., Hernandez and Mazzon, 2007; Venkatesh et al., 2003), thus we only briefly sketch out the relevant theoretical frameworks. Especially in the information systems literature questions related to user technology acceptance have received wide and intense interest. For years researchers have focused on a relatively mutual subset of factors for predicting technology adoption and use: individual attitudes (for example, attitude toward using computers), behavioural intention to use the technology, and personal perceptions (most frequently perceived ease of use and perceived usefulness) (McElroy et al., 2007). These variables are incorporated in the TAM that was first proposed by Davis (1989). TAM has been widely applied in technology acceptance research and is an adaptation of the theory of reasoned action (TRA) (Ajzen and Fishbein, 1980). TRA posits that overt behaviour of a person is predicted by the person’s intentions toward this behaviour, while the behavioural intention is a function of both the attitude toward the behaviour and subjective norms regarding the behaviour (Ajzen and Fishbein, 1980). The Theory of Planned Behaviour (TPB) (Ajzen, 1991) is an(other) expansion of TRA, for situations where people do not have complete control over their behaviour. TPB additionally includes the exogenous variable of perceived behavioural control, a construct that reflects how people perceive the internal and external restrictions to their behaviour (Eriksson et al., 2008). Different from TRA and TPB, general models that are applicable to all kinds of human behaviour,

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TAM was especially designed for the case of users’ acceptance or adoption of new information systems. TAM does not include the construct of subjective norms, because technology adoption is typically assumed to be voluntary, and because the influence of subjective norms is difficult to unravel from effects of attitude on behavioural intention (Davis, 1989). It has been criticised that TAM with its focus on perceived usefulness and perceived ease of use as determinants of the attitude toward using the technology might not adequately consider the potential influence of other psychological and situational factors (Dabholkar and Bagozzi, 2002). Therefore, extensions and modifications of TAM have been suggested by several researchers (for an overview see Chau and Lai, 2003). For example, Gefen et al. (2003) have integrated antecedents of online trust and trust in the online vendor and the technological attribute-based antecedents of intended use found in TAM into an extended theoretical model to explain intended use of a business-to-consumer website. In a similar way, Pavlou (2003) integrated trust toward the web retailer and consumer perceived risk with TAM. A second line of research has investigated acceptance and use of new information technologies from the perspective of the IDT (Moore and Benbasat, 1991; Rogers, 1995). This stream of research also focuses on individual acceptance of technology by using behavioural intention or adoption as dependent variables, but the determinants are usually established according to the characteristics of the new technology (Hernandez and Mazzon, 2007; Venkatesh et al., 2003). Analysing past research on the diffusion of innovation Rogers found that the most important factors influencing technology adoption are user’s perceptions regarding relative advantage, compatibility, complexity, trialability, and observability of the innovation (Rogers, 1995). Most research on the adoption of online banking either builds on TAM models (e.g., Al-Somali et al., 2009; Chau and Lai, 2003; Lassar et al., 2005; Pikkarinen et al., 2004; Sukkar and Hasan, 2005; Wang et al., 2003) or on IDT frameworks (Eriksson et al., 2008; Gerrard and Cunningham, 2003; Ndubisi and Sinti, 2006; Polatoglu and Ekin, 2001). Lassar et al. (2005) integrate TAM and the adoption of innovation framework to predict online banking acceptance. Gounaris and Koritos (2008) investigated the under-utilised perceived characteristics of the innovation framework (an extension of the IDT framework) vis-à-vis the TAM and IDT frameworks and found a significantly improved explanatory power of the former. In this paper, we propose a conceptual model that integrates perceptions of innovation characteristics and individual differences and includes selected constructs from TAM and IDT frameworks [see also Meuter et al. (2005) who have developed a model to explain customer trial of self-service technologies]. The focal dependent variable is actual adoption of online banking.

3

Predictors of online banking adoption

3.1 Individual differences The individual differences that we include in our research model are preference for personal contact, self-efficacy, trust, and socio-demographic characteristics. Preference for personal contact. Previous research on technology-enabled services has shown that some people prefer to deal or interact with people rather than machines and therefore favour service encounters that provide an opportunity for social interaction (Dabholkar and Bagozzi, 2002; Meuter et al., 2005; Walker and Johnson, 2006). On the


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other hand, some people appreciate technology-enabled services precisely because it eliminates the need for personal contact. It can be assumed that consumers who have a high need for interpersonal contact may be averse or reluctant to adopt and use technologically facilitated means of service provisions. Walker and Johnson (2006) found that desire for personal contact has an impact on the usage of online banking, but not on internet shopping. Relying on these findings, we propose that: H1

A high preference for personal contact will decrease the willingness to adopt online banking.

Self-efficacy. As a dimension of perceived behavioural control self-efficacy can be defined as individual judgements of a person’s capabilities to perform a behaviour (Pavlou and Fygenson, 2006). Self-efficacy is associated with beliefs and behaviour and has been shown to have a critical influence on decisions involving computer usage and adoption (Igbaria and Iivari, 1995). Management information systems research suggests that individuals who have high computer self-efficacy are more likely to use information technology (Igbaria and Iivari, 1995; Thatcher et al., 2007). Applied to online banking, self-efficacy reflects the belief that consumers have about their ability to use the computer and the internet effectively to conduct their banking activities (Torkzadeh et al., 2006). We propose that: H2

Self-efficacy is positively related with online banking adoption.

Bank trust and internet trust. Consumer trust is especially important in online transactions (Grabner-Kräuter and Kaluscha, 2003). Online trust is most often defined as a belief or expectation about the website, the web vendor and/or (less frequently) the internet as the trusted party or object of trust or as a behavioural intention or willingness to depend or rely on the trusted party. The prevailing view of consumer trust in the e-commerce literature contends that trust has a direct positive effect on attitudes and behaviour (Jarvenpaa et al., 2004; Pavlou, 2002; Suh and Han, 2003; Teo and Liu, 2007). In the case of online banking, the bank is the web vendor. We propose that: H3

Higher trust in the bank is positively related to online banking adoption.

The analysis of online trust in the context of online banking should not focus exclusively on interpersonal relationships but has to consider impersonal forms of trust as well. Trust in technical systems mainly is based on the perceived functionality (e.g., reliability, capability, correctness and availability) of a system (Lee and Turban, 2001; Thatcher et al., 2007). Internet trust enables favourable expectations that the internet is reliable and predictable and that no harmful consequences will occur if the online consumer uses the internet as a transaction medium for his/her financial transactions (Pavlou and Fygenson, 2006). Therefore, we propose that: H4

Internet trust is positively related to online banking adoption.

Socio-demographic characteristics have often been used to define the online banking customer profile (for an overview see, e.g., Flavián et al., 2006). Variables such as gender, age, educational level, or income level, if significant, would offer easy and efficient ways for banks to segment the market and develop adequate online banking strategies. In their study about consumer attitudes towards bank delivery channels in the UK, Howcroft et al. (2002) found that young consumers value the convenience and time saving potential of online banking more than older consumers. Younger consumers also

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regarded the lack of face-to-face contact as less important than older consumers. The results of a study of mostly Spanish speaking customers also showed that young people were most likely to carry out financial transactions via the internet (Flavián et al., 2006). Furthermore, the findings of this study showed that men were more likely to adopt online banking than women. Karjaluoto et al. (2002) found that the typical user of online banking in Finland is a relatively young, highly educated and wealthy person with good knowledge of the internet. Sarel and Marmorstein (2003) also found that education had a significant effect on online banking adoption among mature Finnish consumers. In line with these studies we assume that age, gender, and education influence the likelihood of adopting online banking of Austrian consumers. We expect that: H5

Younger consumers are more likely to adopt online banking.

H6

Highly-educated people are more likely to adopt online banking.

H7

Males are more likely to adopt online banking.

3.2 Perceptions of innovation characteristics The innovation characteristics that we assume to be most relevant in the online adoption process are relative advantage, complexity, and perceived risk. Relative advantage is the degree to which consumers believe or perceive a new product or service as different from and better than comparable goods (Kolodinsky et al., 2004; Meuter et al., 2005; Moore and Benbasat, 1991; Rogers, 1995). The conceptual meaning of the innovation characteristic relative advantage is similar to perceived usefulness in TAM frameworks. In the case of online banking, relative advantages primarily relate to savings of time and money and convenience. If online banking is perceived as better than alternative banking channels, it is more likely to be adopted. Therefore, we propose that: H8

Perceived relative advantage will positively impact consumer’s adoption of online banking.

Complexity is the extent to which consumers believe or perceive an innovative product or service as easy or difficult to use (Kolodinsky et al., 2004; Meuter et al., 2005; Moore and Benbasat, 1991; Rogers, 1995). The meaning and substance of the innovation characteristic complexity are very similar to perceived ease of use, another construct that according to TAM predicts the acceptance and adoption of new technologies. Perceived ease of use is defined as the degree to which a person believes that using a particular technology would be free of effort (Davis, 1989). In line with previous diffusion and adoption research, we propose that: H9

Consumers who perceive online banking as more complicated or confusing will less likely adopt this service.

Perceived risk of online banking. Previous research suggests to include perceived risk as an important factor influencing online consumer behaviour (Cunningham et al., 2005; Pavlou, 2003; Salam et al., 2003; Schlosser et al., 2006). In several studies a significant negative impact of risk perception on the attitude towards online shopping or likelihood to purchase online was found (Jarvenpaa et al., 2000; Kuhlmeier and Gary, 2005; Laforet


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and Li, 2005; Teo and Liu, 2007). One of the most important categories of perceived risk associated with online banking is security risk related to the potential loss because of deficiencies in the operating system (Awamleh and Fernandes, 2006; Littler and Melanthiou, 2006; Rotchanakitumnuai and Speece, 2003; Sarel and Marmorstein, 2003). Drawing on these findings we posit that: H10 Higher perceived security risk will negatively influence the adoption of online banking.

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Methodology and data

To test the factors which explain the adoption of online banking, data of consumers from different Austrian banks were collected in August 2007. The subjects for the study were randomly selected people that have been approached in different places (airport, park, shopping streets, etc.) in bigger and smaller cities more or less all over Austria. All interviewed consumers had to have a bank account and had to be internet users. These two introductory questions (bank account, internet user) were asked at the beginning of the standardised, self-administered, questionnaires to filter out potential study subjects. As incentives all participants got a soft drink after having fully completed the questionnaire. 381 questionnaires were collected and due to missing data 372 complete datasets could be used for this analysis. Table 1

Sample characteristics Non-adopters

Variables Age

Education

Gender

Adopters

Total

N

%

N

%

N

%

< 20

17

11.1

20

9.1

37

9.9

20–29

24

15.7

62

28.3

86

23.1

30–39

25

16.3

51

23.3

76

20.4

40–49

33

21.6

54

24.7

87

23.4

50–59

28

18.3

24

11.0

52

14.0

60–69

13

8.5

8

3.7

21

5.6

≥ 70

13

8.5

13

3.5

Compulsory education

11

7.2

7

3.2

18

4.8

Apprenticeship

16

10.5

16

7.3

32

8.6

Vocational school

37

24.2

44

20.1

81

21.8

High-school graduation

54

35.3

86

39.3

140

37.6

University degree

35

22.9

66

30.1

101

27.2

Male

66

43.1

118

53.9

184

49.5

Female

87

56.9

101

46.1

188

50.5

153

41.1

219

58.9

372

100.0

340 Table 2

S. Grabner-Kräuter and R.J. Breitenecker Constructs and items

Construct/items Preference for personal contact (adapted from Walker and Johnson, 2006) I am more reassured by dealing face-to-face with customer service people. My particular service requirements are better served by people. I prefer face-to-face contact to explain what I want and to answer my questions. I feel like I am more in control when dealing with customer service people than with automated systems. Self-efficacy (adapted from Lassar et al., 2005) How comfortable do you feel using the computer? How comfortable do you feel using the internet? Bank trust (adapted from Bhattacherjee, 2002; McKnight et al., 2002; Schlosser et al., 2006) My bank is fair in its conduct of customer transactions My bank is honest and sincere. My bank is open and receptive to customer needs. If I need help my bank would try to support me. My needs and wants are important to my bank. Internet trust (adapted from Chaudhuri and Holbrook, 2001; McKnight et al., 2002) With adequate safety measures on a website I don’t hesitate to enter my credit card information (willingness to depend on the internet) I trust the internet (willingness to depend on the internet) When performing a transaction on the internet I know exactly what will happen (trusting beliefs – predictability) Internet transactions (e.g., online shopping or online hotel reservations) always function as expected (trusting beliefs – reliability) Perceived relative advantage (adapted from Lee et al., 2005) Internet banking makes it easier for me to conduct my banking transactions Internet banking is convenient. Internet baking eliminates geographic constraint. Using internet banking will reduce my transaction costs such as fees paid to banks. Perceived complexity (adapted from Meuter et al., 2005) I think it is cumbersome to use internet banking. It is difficult to use internet banking. I think it is easy to adopt internet banking. (reversed) Perceived security risk of internet banking (adapted from Awamleh and Fernandes, 2006; Meuter et al., 2005) I am confident about the security aspects of internet banking in Austria. I am afraid others will get access to information concerning my internet banking transactions. I am afraid that the confidentiality of my financial transactions might get lost when using internet banking. I think that privacy is not guaranteed when using internet banking.

CONTACT1 CONTACT2 CONTACT3 CONTACT4

SE_Comp SE_Inter

TRUSTB_i1 TRUSTB_i2 TRUSTB_b1 TRUSTB_b2 TRUSTB_b3

ITRUST1 ITRUST2 ITRUST3 ITRUST4

IC_RA1 IC_RA2 IC_RA3 IC_RA4

IC_COMP1 IC_COMP2 IC_COMP3

RISK_IB1 RISK_IB2 RISK_IB3 RISK_IB4


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The sample consists of 153 (41.1%) non-adopters and 219 (58.9%) adopters of online banking. The sample splits into 184 (49.5%) men and 188 (50.5%) women. We measured age of the persons on a seven-point ordinal scale. The youngest and oldest persons in the sample are less than 20 and more than 70 years old. The majority of the respondents are between 40 to 49 years (23.4%) and 20 to 29 years (23.1%) followed by the group of 30 to 39 year old consumers (20.4%). The educational levels of the surveyed consumers range from compulsory school to university degree. The largest group are respondents with high-school graduation (37.6%) followed by persons with university degree (27.2%). We can report a significant relationship between gender and adoption of online banking (Fisher’s exact test, p-value = .046). Online banking users are more male than female. There are also significant differences between adopters and non-adopters of online banking concerning age and education. On average, online banking users are younger and have a higher educational level (Mann-Whitney test, p-value < .001 and p-value = .016). Table 1 summarises the sample characteristics. All measurement items of the constructs were drawn from the literature and adapted to the online banking context, if necessary (see Table 2). The items were measured on a seven-point Likert-type scale. To establish construct validity and reliability of measurement items and to test the measurement model exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted. We started our analysis with an EFA (principal components, with KAISER criteria and varimax rotation with Kaiser Normalisation) to assess the unidimensionality of the construct items. The EFA identified six factors explaining 75.5% of data variance. Each set of construct items – with the exception of self-efficacy and internet trust – loaded on one factor for their respective constructs. The items of the theoretically different constructs of self-efficacy and internet trust loaded on one factor, but showed differences in the extent of the loadings. The lower loadings of items from internet trust indicate that the items of self-efficacy and internet trust belong to different constructs. Next, the scales that were obtained were subjected to a CFA using AMOS 16. The initial measurement model of the CFA with 26 items and seven constructs shows a mediocre to adequate model fit. Two items had loadings less than .7, which indicates that the corresponding factors explain less than 50% of the item variances. These two items showed minor loadings and one of it showed cross loadings in the EFA, thus we decided to remove these two items from the measurement model (see Table 3). All other indicators loaded significantly on their hypothesised latent variables and no significant cross-loadings existed. After removing the two items the goodness of fit statistics of the CFA improved and the fit of the model was adequate. The chi-square value of the final model is about 384.19 (p-value = .000, X2 / df = 1.663). The chi-square value tends to be significant with larger sample size, as in this case. This means that the test statistic is significant when there are only trivial differences. Alternative goodness of fit statistics like the goodness of fit index (GFI) and the normed fit index (NFI) are .920 and .948 respectively, and thus above the required threshold of .9 for an adequate fit. The Tucker-Lewis index (TLI) and the comparative fit index (CFI) are .974 and .978. The root mean square error of approximation (RMSEA) is about .042 and below the recommended maximum threshold of .5. Thus an adequate fit of the model is given. (See Table 4)

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Table 3

Factor loadings of the initial and final model and indicator reliability of final model

Construct

Item

Preference for personal contact

Self-efficacy Bank trust

Internet trust

Relative advantage

Complexity of IB

Perceived security risk of IB

Table 4

Factor loadings

Indicator reliability

Initial model

Final model

Final model

CONTACT1

.859

.859

.738

CONTACT2

.850

.850

.723

CONTACT3

.832

.832

.692

CONTACT4

.925

.925

.856

SE_Comp

.853

.853

.728

SE_Inter

.999

.999

.998

TRUSTB_i1

.768

.768

.590

TRUSTB_i2

.778

.778

.605

TRUSTB_b1

.838

.838

.702

TRUSTB_b2

.836

.835

.697

TRUSTB_b3

.878

.878

.771

ITRUST1

.814

.814

.663

ITRUST2

.794

.794

.630

ITRUST3

.830

.830

.689

ITRUST4

.773

.774

.599

IC_RA1

.912

.904

.817

IC_RA2

.955

.965

.931

IC_RA3

.839

.837

.701

IC_RA4

.480

IC_COMP1

.901

.908

.824

IC_COMP2

.958

.954

.910

IC_COMP3

.609

RISK_IB1

.801

.801

.642

RISK_IB2

.847

.847

.717

RISK_IB3

.942

.942

.887

RISK_IB4

.868

.868

.753

Model fit measures for the initial and final model

Chi-square df p-value Chi-square/df GFI NFI TLI CFI RMSEA

Initial model

Final model

546.61 278 .000 1.966 .901 .930 .958 .964 .050

384.19 231 .000 1.663 .920 .948 .974 .978 .042


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Following Hair (2006) construct validity is made up of the four components convergent validity, discriminate validity, nomological validity and content validity. We have tested all four dimensions of validity in our analysis. Convergent validity was tested by inspection of the high of factor loadings, the variance extracted and by calculating the composite reliability (CR) and Cronbach alpha. Discriminate validity was tested by comparing the variance extracted percentages for all constructs with the maximum of squared correlations of the constructs with other constructs (Fornell Larcker ratio < 1). No squared inter-correlation is higher than the average variance extracted, thus discriminate validity is given. Because of the use of borrowed and tested scales content validity can be assumed. In addition we have checked the meaning of all items relating to the constructs. (See Table 5) Table 5

Reliability measures of final constructs Cronbach alpha

Composite reliability

Average variance extracted

Fornell-Larcker ratio


.923

.924

.752

.238

Bank trust

.911

.911

.673

.022

Internet trust

.876

.879

.645

.443

Relative advantage

.927

.930

.816

.336

Perceived security risk of OB

.920

.923

.750

.362

Complexity of OB

.934

.929

.867

.229

Self-efficacy

.919

.926

.863

.331

To test nomological validity we have checked the correlations between constructs (Table 6). All correlations seem to be plausible. Hence also nomological validity can be assumed. All reliability and validity measures are fulfilled for the applied constructs, thus construct validity and reliability of the measurement model can be assumed. After testing our measurement model we calculated item mean scores to represent the construct values for the binary logistic regression analysis. Table 6

Correlations of constructs Age

(2)

(3)

(4)

(5)

(6)

(2) Education

–.017

(3) Preference for personal contact

.239

–.147

(4) Bank trust

.121

–.108

.085

(5) Internet trust

–.307

.130

–.423

.050

(6) Relative advantage

–.218

.156

–.353

.024

(7) Perceived security risk of OB

.102

–.087

.395

–.109 –.521 –.286

(8) Complexity of OB

.052

–.168

.369

–.071 –.445 –.396

(9) Self-efficacy

–.460

.218

–.365 –.003

(7)

(8)

.524

.535

.324

.415 –.287 –.362

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5


Empirical analysis

We tested all applied constructs for differences in mean values between non-adopters and adopters of online banking via T-test. For all constructs with the exception of bank trust we found highly significant differences between these two groups of consumers. Compared to non-adopters, adopters of online banking have a lower preference for personal contact and a lower perceived meaning about the complexity and security risk of online banking. Adopters have a higher self-efficacy, higher trust into the internet, are more technology orientated, and perceive a higher relative advantage of online banking than non-adopters do (see Table 7). Table 7

T-test results for mean differences between non-adopters and adopters Non-adopters

Adopters

Differences

p-value


5.827

4.191

1.636***

< .001

Bank trust

5.668

5.690

–0.022

0.856

Internet trust

3.356

5.126

–1.769***

< .001

Relative advantage

5.017

6.409

–1.392***

< .001


4.925

3.553

1.372***

< .001

Complexity of OB

3.582

2.084

1.497***

< .001

Self-efficacy

3.255

3.998

–0.743***

< .001

The profile plot in Figure 1 illustrates the differences between adopters and non-adopters. The highest mean differences can be reported for preference for personal contact, internet trust and perceived complexity of online banking. Bank trust is alike for adopters and non-adopters. Figure 1

Profile plot adopters and non-adopters


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We applied a binary logistic regression analysis to test our hypotheses using SPSS. A logistic regression model was chosen due to the dichotomy character of the dependent variable adoption of online banking (0 = non-adopter, 1 = adopter). The logistic regression models the probability belonging to the class of online banking users. We included a dummy variable for gender in our regression model. The dummy variable is 1 if the person is a man and 0 if the person is a woman. Item mean scores represent the construct values. Therefore the baseline in the model is a female person with average values for all numeric variables. The Cox and Snell and Nagelkerke R-squared values are about 0.415 and 0.559, respectively. The R-squared measures indicate that there is an adequate fit of the model. The estimated model classifies 80.6% of the overall sample correctly, using a cut-off value for the predicted probability of 0.5. Further, 84.9% of the adopters and 74.5% of the non-adopters are classified correctly by the model. The receiver operating characteristic (ROC) curve can be used to summarise the predictive power of the logistic regression model. The concordance index which is equivalent to the area under the ROC curve shows a value of 0.894, which states an excellent performance of the estimated logistic regression model (Hosmer and Lemeshow, 2000). Table 8

Results of the logistic regression analysis

Variables

B

S.E.

Wald

Sig.

Exp(B)

Gender (man)

0.636*

0.300

4.499

0.034

1.890

Age

–0.111

0.112

0.977

0.323

0.895

Education

0.065

0.139

0.220

0.639

1.067

–0.325**

0.099

10.721

0.001

0.723

–0.011

0.126

0.007

0.931

0.989

Preference for personal contact Bank trust Internet trust

0.412**

0.133

9.556

0.002

1.510

Relative Advantage

0.665***

0.138

23.306

0.000

1.945


–0.272*

0.118

5.265

0.022

0.762

Complexity of OB

–0.144

0.094

2.310

0.129

0.866

Self-efficacy

0.041

0.194

0.044

0.834

1.041

Constant

–1.064

1.716

0.385

0.535

0.345

Number of observations –2 Log likelihood

372 304.477

Cox and Snell R-square

0.415

Nagelkerke R-square

0.559

Correct cases classified: Cut-value: 0.5

Adopters

Non-adopters

Overall sample

84.9%

74.5%

80.6%

Notes: Binary logistic regression with dependent variable: internet banking adoption 0 = ‘non-adopters’ and 1 = ‘adopters’ Level of significance: *p < 0.05, **p < 0.01 and ***p < 0.001

The significance of individual variables was tested by the Wald statistic. The results of the logistic regression analysis indicate that the coefficients of preference for personal contact and the perceived security risk of online banking are significantly negative. A person who has a higher preference for personal contact in service encounters will have a

346


lower probability to become an online banking user. The same is true for persons who perceive a high security risk in the usage of online banking. Thus, hypotheses H1 and H10 have to be accepted. The coefficients for self-efficacy (H2) as well as for bank trust (H3) show no significant relationship in the logistic regression model. Thus, these hypotheses have to be rejected. Internet trust has a positive significant coefficient (1% level). The coefficient for relative advantage is positive at a significance level of 0.1%. Thus, hypotheses H4 and H8 are supported. The estimated parameter for the construct of complexity of online banking is not significant. Hence, hypothesis H9 has to be rejected. From the personal characteristics only the coefficient of one variable is significant to the level of 5%. The dummy variable for male consumers is significantly positive, indicating that the probability of getting an online banking user is higher for men than for women. Thus, hypothesis H7 can be supported. Although there were significant differences in age and education concerning the adoption of online banking in the bivariate tests, the parameters of the variables age and education are not significant in our regression model. Hypotheses H5 and H6 have to be rejected. Table 8 summarises the results of the binary logistic regression analysis. Table 9

Changes in the probability to adopt (non-adopt) online banking

Reference model

Adopters

Non-adopters

55.7%

44.3%

Gender (man)

70.4%

29.6%

Age (under 20) = 1

62.1%

37.9%

Age (70+) = 7

45.8%

54.2%

Education = 1

51.3%

48.7%

Education = 5

57.7%

42.3%

Preference for personal contact = 1

81.5%

18.5%

Preference for personal contact = 7

38.6%

61.4%

Bank trust = 1

57.0%

43.0%

Bank trust = 7

55.3%

44.7%

Internet trust = 1

23.7%

76.3%

Internet trust = 7

78.6%

21.4%

Relative advantage = 1

4.8%

95.2%

Relative advantage = 7

73.2%

26.8%

Perceived security risk of OB = 1

74.6%

25.4%

Perceived security risk of OB = 7

36.5%

63.5%

Complexity of OB = 1

61.6%

38.4%

Complexity of OB = 7

40.4%

59.6%

Self-efficacy = 1

53.0%

47.0%

Self-efficacy = 7

59.0%

41.0%

The changes in the probability of online banking adoption (or non-adoption) also provide an indication of the importance of the predicting variables. Table 9 summarises the changes in the probability of adoption (non-adoption) compared to the reference model with mean values for all variables, if the value of the respective variable is changed to the


347

minimum value (= 1) or maximum value (= 7). In the reference model gender is female. For instance, if preference for personal contact is very low (the value is changed to 1, all other variables remain unchanged), the probability to adopt online banking changes from 55.7% in the reference model to 81,5%, and the probability of non-adoption changes from 44.3% to 18.5%. The probability changes for perceived relative advantage are even more impressive. If the value of perceived relative advantage is changed to the minimum value of 1, the probability of online banking adoption changes to less than 5% and the probability of non-adoption is higher than 95%. On the other hand, the changes in online banking adoption are very low if the values for self-efficacy or bank trust are changed to their minima and maxima, respectively.

6

Discussion and implications

The research model and results contribute to a better understanding of the factors that influence online banking adoption. First, our results confirm the importance of perceived innovation characteristics in the online banking adoption process. Beyond that, our findings suggest that internet trust and preference for personal contact are individual difference variables that determine consumers’ online banking adoption. Thus, the importance of perceived innovation characteristics and individual differences in online banking adoption process was confirmed. We found preference for personal contact, internet trust, perceived relative advantage, and perceived security risk of online banking to be the most important predictors of online banking adoption. Another contribution of this study is that trust toward the bank (or the internet vendor in a broader sense) and trust toward the internet must not be confounded or treated as different dimensions of the same construct ‘online trust’, but have to be regarded as two distinct constructs that influence online consumer behaviour in different ways. While internet trust has a significant positive impact on online banking adoption, bank trust is not related to online banking adoption. Recommendations in the literature on online banking concerning the design of user-friendly and trust-inducing websites might not be sufficient to overcome consumers’ reluctancy to conduct their financial and other economic transactions on the internet. Consumers might refrain from visiting websites designed for e-commerce or online banking because they either do not consider the internet infrastructure as reliable and secure or they have a greater desire for personal contact and interaction. To attract more online banking customers and increase the acceptance of online banking services in Austria, it is definitely not enough to make the online banking system convenient and easy to interact with. Rather it is of paramount importance, to address the issue of security in order to improve the rate of online banking adoption (see also Laforet and Li, 2005; Mukherjee and Nath, 2003). To ensure the security of their online banking systems banks use security features such as firewalls, filtering routers, callback modems, encryption biometrics, smart cards, and digital certification and authentication (Mukherjee and Nath, 2003). However, for the majority of consumers it is beyond the scope of their technological understanding to fully comprehend the meaning and functionality of these security features. Therefore the attention of bank managers might be fruitfully focused on training and promotion approaches with the aim to influence their customers’ perception of online security and to improve their customers’ knowledge about privacy and security mechanisms and concepts such as encryption methods.

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In their campaigning to persuade non-adopters, banks should re-emphasise and further accentuate the advantages of online banking and provide detailed information about its convenience, simplicity, flexibility, economic benefits and controllability. Banks also should present comprehensive information about the use of security features already in the instruction phase and provide regular information up-dates for customers about security improvements. Beyond that, banks could organise high-publicity events such as public lectures or research grants to communicate continuing efforts to improve online banking security.

References Ajzen, I. (1991) ‘The theory of planned behavior’, Organizational Behavior and Human Decision Processes, Vol. 50, No. 2, pp.179–211. Ajzen, I. and Fishbein, M. (1980) Understanding Attitudes and Predicting Social Behavior, Englewood Cliffs, NJ, Prentice-Hall. Al-Somali, S.A., Gholami, R. and Clegg, B. (2009) ‘An investigation into the acceptance of online banking in Saudi Arabia’, Technovation, Vol. 29, No. 2, pp.130–141. Awamleh, R. and Fernandes, C. (2006) ‘Diffusion of internet banking amongst educated consumers in a high income non-OECD country’, Journal of Internet Banking and Commerce, Vol. 11, No. 3. Bhattacherjee, A. (2002) ‘Individual trust in online firms: scale development and initial test’, Journal of Management Information Systems, Vol. 19, No. 1, pp.211–241. Chau, P.Y.K. and Lai, V.S.K. (2003) ‘An empirical investigation of the determinants of user acceptance of internet banking’, Journal of Organizational Computing and Electronic Commerce, Vol. 13, No. 2, pp.123–145. Chaudhuri, A. and Holbrook, M.B. (2001) ‘The chain of effects from brand trust and brand affect to brand performance: the role of brand loyalty’, Journal of Marketing, April, Vol. 65, pp.81–93. Cunningham, L.F., Gerlach, J. and Harper, M.D. (2005) ‘Perceived risk and e-banking services: an analysis from the perspective of the consumer’, Journal of Financial Services Marketing, Vol. 10, No. 2, pp.165–178. Dabholkar, P.A. and Bagozzi, R. (2002) ‘An attitudinal model of technology-based-self-service: Moderating effects of consumer traits and situational factors’, Journal of the Academy of Marketing Science, Vol. 30, No. 3, pp.184–201. Davis, F.D. (1989) ‘Perceived usefulness, perceived ease of use, and user acceptance of information technology’, MIS Quarterly, Vol. 13, No. 3, pp.318–340. Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989) ‘User acceptance of computer technology: a comparison of two theoretical models’, Management Science, Vol. 35, No. 8, pp.982–1002. Eriksson, K., Kerem, K. and Nilsson, D. (2008) ‘The adoption of commercial innovations in the former Central and Eastern European markets. The case of internet banking in Estonia’, International Journal of Bank Marketing, Vol. 26, No. 3, pp.154–169. Flavián, C., Guinalíu, M. and Torres, E. (2006) ‘'How bricks-and-mortar attributes affect online banking adoption’, International Journal of Bank Marketing, Vol. 24, No. 6, pp.406–423. Gefen, D., Karahanna, E. and Straub, D.W. (2003) ‘Trust and tam in online shopping: an integrated model’, MIS Quarterly, Vol. 27, No. 1, pp.51–90. Gerrard, P. and Cunningham, B.J. (2003) ‘The diffusion of internet banking among Singapore consumers’, International Journal of Bank Marketing, Vol. 21, No. 1, pp.16–28. Gounaris, S. and Koritos, C. (2008) ‘Investigating the drivers of internet banking adoption decision: a comparison of three alternative frameworks’, International Journal of Bank Marketing, Vol. 26, No. 5, pp.282–304.


349

Grabner-Kräuter, S. and Faullant, R. (2008) ‘Consumer acceptance of internet banking: the influence of internet trust’, International Journal of Bank Marketing, Vol. 26, No. 7, pp.483–504. Grabner-Kräuter, S. and Kaluscha, E.A. (2003) ‘Empirical research in online trust: a review and critical assessment’, International Journal of Human-Computer-Studies, Vol. 58, No. 6, pp.783–812. Hair, J.F. (2006) Multivariate Data Analysis, Upper Saddle River, NJ, Pearson Prentice-Hall. Hernandez, J.M.C. and Mazzon, J.A. (2007) ‘Adoption of internet banking: proposition and implementation of an integrated methodology approach’, International Journal of Bank Marketing, Vol. 25, No. 2, pp.72–88. Hosmer, D.W. and Lemeshow, S. (2000) Applied Logistic Regression, 2nd ed., New York, Wiley. Howcroft, B., Hamilton, R. and Hewer, P. (2002) ‘Consumer attitude and the usage and adoption of home-based banking in the United Kingdom’, International Journal of Bank Marketing, Vol. 20, No. 3, pp.111–121. Igbaria, M. and Iivari, J. (1995) ’The effects of self-efficacy on computer usage’, Omega, Vol. 23, No. 6, pp.587–605. Jarvenpaa, S.L., Shaw, T.R. and Staples, S. (2004) ‘Toward contextualized theories of trust: the role of trust in global virtual teams’, Information Systems Research, Vol. 15, No. 3, pp.250–267. Jarvenpaa, S.L., Tractinsky, N. and Vitale, M. (2000) ‘Consumer trust in an internet store’, Information Technology and Management, Vol. 1, Nos. 1–2, pp.45–71. Karjaluoto, H., Mattila, M. and Pento, T. (2002) ‘Factors underlying attitude formation towards online banking in Finland’, International Journal of Bank Marketing, Vol. 20, No. 6, pp.261–272. Kolodinsky, J.M., Hogarth, J.M. and Hilgert, M.A. (2004) ‘The adoption of electronic banking technologies by US consumers’, The International Journal of Bank Marketing, Vol. 22, No. 4, pp.238–259. Kuhlmeier, D. and Gary, K. (2005) ‘Antecedents to internet-based purchasing: a multinational study’, International Marketing Review, Vol. 22, No. 4, pp.460–473. Laforet, S. and Li, X. (2005) ‘Consumers’ attitudes towards online and mobile banking in China’, International Journal of Bank Marketing, Vol. 23, No. 5, pp.362–380. Lai, V.S. and Li, H. (2005) ‘Technology acceptance model for internet banking: an invariance analysis’, Information & Management, Vol. 42, No. 2, pp.373–386. Lassar, W.M., Manolis, C. and Lassar, S.S. (2005) ‘The relationship between consumer innovativeness, personal characteristics, and online banking adoption’, International Journal of Bank Marketing, Vol. 23, No. 2, pp.176–199. Lee, E-J., Kwon, K-N. and Schumann, D.W. (2005) ‘Segmenting the non-adopter category in the diffusion of internet banking’, International Journal of Bank Marketing, Vol. 23, No. 5, pp.414–437. Lee, M.K.O. and Turban, E. (2001) ‘A trust model for consumer internet shopping’, International Journal of Electronic Commerce, Vol. 6, No. 1, pp.75–91. Littler, D. and Melanthiou, D. (2006) ‘Consumer perceptions of risk and uncertainty and the implications for behaviour towards innovative retail services: the case of internet banking’, Journal of Retailing and Consumer Services, Vol. 13, No. 6, pp.431–443. Luarn, P. and Lin, H-H. (2005) ‘Toward an understanding of the behavioral intention to use mobile banking’, Computers in Human Behavior, Vol. 21, No. 6, pp.873–891. McElroy, J.C., Hendrickson, A.R., Townsend, A.M. and DeMarie, S.M. (2007) ‘Dispositional Factors in internet use: personality versus cognitive style’, MIS Quarterly, Vol. 31, No. 4, pp.809–820.

350


McKnight, D.H., Choudhury, V. and Kacmar, C. (2002) ‘Developing and validating trust measures for e-commerce: an integrative typology’, Information System Research, Vol. 13, No. 3, pp.334–359. Meuter, M.L., Bitner, M.J., Ostrom, A.L. and Brown, S.W. (2005) ‘Choosing among alternative service delivery modes: an investigation of customer trial of self-service technologies’, Journal of Marketing, Vol. 69, No. 2, pp.61–83. Meyer, T. (2006) ‘Online banking. What we learn from differences in Europe’, in Deutsche Bank Research (Ed.): E-Banking Snapshot, 16 February 2006, Deutsche Bank AG, Frankfurt am Main. Moore, G.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. Mukherjee, A. and Nath, P. (2003) ‘A model of trust in online relationship banking’, International Journal of Bank Marketing, Vol. 21, No. 1, pp.5–15. Ndubisi, N.O. and Sinti, Q. (2006) ‘Consumer attitudes, system’s characteristics and internet banking adoption in Malaysia’, Management Research News, Vol. 29, Nos. 1/2, pp.16–27. Pavlou, P.A. (2002) ‘What drives electronic commerce? A theory of planned behavior perspective’, Academy of Management Proceedings, Vol. 2002, pp.A1–A6. Pavlou, P.A. (2003) ‘Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model’, International Journal of Electronic Commerce, Vol. 7, No. 3, pp.101–134. Pavlou, P.A. and Fygenson, M. (2006) ‘Understanding and predicting electronic commerce adoption: an extension of the theory pf planned behavior’, MIS Quarterly, Vol. 30, No. 1, pp.115–143. Pikkarinen, T., Pikkarinen, K., Karjaluoto, H. and Pahnila, S. (2004) ‘Consumer acceptance of online banking: an extension of the technology acceptance model’, Internet Research, Vol. 14, No. 3, pp.224–235. Polatoglu, V.N. and Ekin, S. (2001) ‘An empirical investigation of the Turkish consumers’ acceptance of internet banking services’, International Journal of Bank Marketing, Vol. 19, No. 4, pp.156–165. Ravi, V., Carr, M. and Sagar, N.V. (2006) ‘Profiling of internet banking users in India using intelligent techniques’, Journal of Services Research, Vol. 6, No. 2, pp.61–73. Rogers, E. (1995) Diffusion of Innovations, Free Press, New York. Rotchanakitumnuai, S. and Speece, M. (2003) ‘Barriers to internet banking adoption: a qualitative study among cooperate customers in Thailand’, International Journal of Bank Marketing, Vol. 21, No. 6, pp.312–323. Salam, A.F., Rao, H.R. and Pegels, C.C. (2003) ‘Consumer-perceived risk in e-commerce transactions’, Communications of the ACM, Vol. 46, No. 12, pp.325–331. Sarel, D. and Marmorstein, H. (2003) ‘Marketing online banking services: the voice of the customer’, Journal of Financial Services Marketing, Vol. 8, No. 2, pp.106–118. Sayar, C. and Wolfe, S. (2007) ‘Internet banking market performance: Turkey versus the UK’, International Journal of Bank Marketing, Vol. 25, No. 3, pp.122–141. Schlosser, A.E., White, T.B. and Lloyd, S.M. (2006) ‘Converting website visitors into buyers: how website investment increases consumer trusting beliefs and online purchase intentions’, Journal of Marketing, Vol. 70, No. 2, pp.133–148. Suh, B. and Han, I. (2003) ‘The impact of customer trust and perception of security control on the acceptance of electronic commerce’, International Journal of Electronic Commerce, Vol. 7, No. 3, pp.135–161. Sukkar, A.A. and Hasan, H. (2005) ‘Toward a model for the acceptance of internet banking in developing countries’, Information Technology for Development, Vol. 11, No. 4, pp.381–398.


351

Teo, T.S.H. and Liu, J. (2007) ‘Consumer trust in e-commerce in the United States, Singapore and China’, Omega, Vol. 35, No. 1, pp.22–38. Thatcher, J.B., Loughry, M.L., Lim, J. and McKnight, D.H. (2007) ‘Internet anxiety: an empirical study of the effects of personality, beliefs, and social support’, Information & Management, Vol. 44, No. 4, pp.353–363. Torkzadeh, G., Chang, JC-J. and Demirhan, D. (2006) ‘A contingency model of computer and internet self-efficacy’, Information & Management, Vol. 43, No. 4, pp.541–550. Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003) ‘User acceptance of information technology: toward a unified view’, MIS Quarterly, Vol. 27, No. 3, pp.425–478. Walker, R.H. and Johnson, L.W. (2006) ‘Why consumers use and do not use technology-enabled services’, Journal of Services Marketing, Vol. 20, No. 2, pp.125–135. Wang, Y-S., Wang, Y-M., Lin, H-H. and Tang, T-I. (2003) ‘Determinants of user acceptance of internet banking: an empirical study’, International Journal of Service Industry Management, Vol. 14, No. 5, pp.501–519. White, H. and Nteli, F. (2004) ‘Internet banking in the UK: Why are there not more customers?’, Journal of Financial Services Marketing, Vol. 9, No. 1, pp.49–56.