Customer channel choices in retail banking: A test of individual differences for transactional services

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1 Customer channel choices in retail banking: A test of individual differences for transactional services Ragnvald Sannes Handelshøyskolen BI Institutt for Teknologiledelse Postboks Sandvika ragnvald.sannes@bi.no Home page: infomgt.bi.no/sannes ABSTRACT With the introduction of self-service in the financial sector there have been made many assumptions about customer behaviour. Both practitioners and researchers have examined and proposed heuristics for distinguishing self-service customers from other customers. In banking this divide is usually made between Internet segments and branch segments (e.g. Mols, 1999). This paper is part of a research project where these assumptions are challenged. First, customers channel choices are viewed as dependent upon characteristics of the transaction (or service), characteristics of the channel, characteristics of the customer relationship and individual differences. Second, other self-service channels than the Internet are included. The focus in this paper is to report the results of an analysis on the effect of individual differences on customer channel choice for transactional services. The analysis is based on transaction and customer data from two branch offices of a Swedish bank, and the analysis is focussed on the data available can cast light on factor that influence adoption and use of self-service channels. INTRODUCTION The introduction of Internet-based self-services in banking is expected to lead to changes in customer behaviour. Any effort to develop and promote new and current services in new information technology-enabled channels should be guided by knowledge about how customers adopt and use these new channels. The main purpose of this paper is to develop knowledge about determinants of customers channel choices in retail banking. There has been argued that there is a digital divide based on demographic variables between users and non-users of the Internet. Recent studies from the US indicate that households with a high family income and high education level are more likely to have a computer access at home with Internet connection (Hoffmann & Novak, 1998; Norris, 2000). 1 Studies of Internet use in Sweden suggest that Internet use is related to age, sex and education (Holst, 1999:221). 1 These studies also indicate a digital divide explained by race for low income households where African Americans have a lower computer access and general Web use than whites. These differences are not significant for households with medium or high income. 119

2 The 1995 Survey of Consumer Finances in the US revealed that adoption and use of electronic channels are related to income, level of financial assets, age and education (Kennickel & Kwast, 1997). SIFO found differences in rate of adoption of Internet bank related to age and sex in a recent poll in Sweden (SIFO, 2000). Currently, around 25% of bank customers have Internet banking services (SIFO, 2000). Both the US and the Swedish studies indicate that many customers have not yet, and are not likely to, adopt new electronic channels for self-services. These consistent findings indicate that bank customers can be divided into an Internet segment and a branch segment using demographic models to categorize customers into segments (Mols, 1999). The simplicity of accurate predictions of customer behavior based on demographics is challenged by customer segmentation research that has found that actionable segmentation models in general do not fit into demographic models (Forsyth, Gupta, Haldar, Kaul, & Kettle, 1999). Empirically tested behavioral theories such as the theory of reasoned action (Fishbein & Ajzen, 1975) suggest a relationship between attitudes and behavior. User evaluations of technology that taps the user s attitudes toward technology in a measurement of task-technology fit (Goodhue & Thompson, 1995). Research on self-service technology emphasize the significance of the service encounter itself on channel choices (e.g. Bitner, Brown, & Meuter, 2000; Dabholkar, 1996). Research on electronic communication and media choice assume that there is a relationship between message properties and media choice (e.g. Daft & Lengel, 1984; Zmud, Lind, & Young, 1990). Hence, the channel choice is likely to be influenced by properties of the technology and properties of the service suggesting that the branch is a preferred channel for services involving complex communication (e.g. advisory services) while self-service technology would be preferred for simple transaction services and account information. Further developments in media choice research suggest that media choice may be context sensitive (e.g. Yates & Orlikowski, 1992). One implication of these findings for the channel choice decision for banking services would be that all payment transactions are not equal, suggesting that the channel choice decision is contextual. As customers channel choices are related to characteristics of the individual, the channel, the service and the context it is a fairly complex task to categorize customers into segments predicting their channel choice behavior. Current segmentation models need to be expanded in order to include other determinants of the channel choice decision. A competitive challenge for the banking industry is to succeed in matching customer profiles, channels and services in order to develop a service/channel matrix that fits customer preferences. This includes understanding the information requirements necessary in order to make such a segmentation. Research on determinants of customers channel choices is valuable input to these competitive challenges. BACKGROUND The traditional literature on adoption of information technology examines a variety of solutions in a proprietary setting, e.g. the adoption and use of information systems developed in-house (e.g. Kwon & Zmud, 1987) and standard workstation technology (e.g. Moore & Benbasat, 1991) based on traditional innovation adoption/diffusion models (e.g. Rogers, 1983). This study proposes to use this literature along with literature on self-service 120

3 technology, use of Internet banking services and electronic communication to study customers channel choices for transaction services in retail banking. The review of literature is organised into sections on individual characteristics, service characteristics, and channel characteristics. Individual characteristics In the first section we concluded that demographic variables that influence the adoption and use of Internet and Internet banking services include age, sex, income, level of financial assets, education. Other individual characteristics, in particular experience and psychological traits, have been found to influence adoption of innovations (Rogers, 1983). Age Results from research on the influence of age on adoption and use of computers, the Internet and new banking services is mixed. The 1995 Survey of Consumer Finances found use of electronic banking services delivered through the computer to be more likely in households with household heads under the age of 35 (Kennickel & Kwast, 1997). The results were more inconclusive for other electronic banking services. A recent Swedish study suggest a similar threshold effect for adoption of Internet banking for customers under the age of 65, with the highest rate of adoption in the group (SIFO, 2000). These findings are consistent with data on the adoption and use of the Internet in Sweden (Holst, 1999). Differences in unit of analysis (customer age and age of household head) cannot count for the differences in these findings. The conclusion is therefore that age is not likely to be a strong determinant for adoption of self-service technologies but we cannot rule out that it has an impact. Sex The adoption of new technology is often referred to as a male thing as men in general are regarded to be more technologically oriented than women. Swedish data suggest a higher adoption rate of Internet services in general among men than among women (Holst, 1999) as well as for Internet banking services (SIFO, 2000). We can therefore conclude that there is a potential gender effect when it comes to adoption of new technology-based services. Income The 1995 Survey of Consumer Finance found a weak positive association between income and use of electronic banking services. Households with incomes below $25,000 per year were unlikely to use electronic services while households with annual income above $50,000 are likely to do so (Kennickel & Kwast, 1997). We have not found any corresponding data within the Swedish context. The US study also found income to be correlated to age, education and financial assets that were more important independent factors than income. Level of financial assets Level of financial assets are positively correlated to the rate of adoption of electronic banking services (Kennickel & Kwast, 1997). 121

4 Education Length of education is positively correlated to adoption of electronic banking services, with an important break point at a college degree (Kennickel & Kwast, 1997). Data on adoption of Internet services in Sweden reveal a similar pattern (Holst, 1999). Personality traits Personality traits are variables that are based on individual differences that may have an impact upon channel choice. Based on review articles in several fields we have concluded that the Kirton s adaptor-innovator inventory (Kirton, 1976; Kirton, 1989) is relevant as it is a stable and reliable scale that may explain individual differences in the adoption of new channels or not. The other relevant scale is the short form of need for cognition (Cacioppo, Petty, Feinstein, & Jarvis, 1996) as the need for cognition scale measure individual differences in information search and processing that may influence channel selection if these are perceived to have differences in media richness (e.g. Daft & Lengel, 1984; Yates, Orlikowski, & Okamura, 1999; Zmud et al., 1990). Data on these scales are not available for our segmentation, but will be included in a forthcoming customer survey in this research project. Service characteristics One implication of the conclusion in the introduction is that customers channel choices can be different for different services. Building on media richness theory (Daft & Lengel, 1984) 2, channel choice can be viewed a rational process where service-channel fit is essential to effective service delivery. The basic assumption in this view is that a channel s ability to carry out a service effective depend on the fit between service characteristics and channels characteristics. Banking services can then, according to media richness theory, be characterised by their uncertainty and equivocality. Uncertainty is defined as when an individual knows how to carry out a service, but do not have sufficient data to so. Equivocality is defined as ambiguity in how to carry out a service. Successful service delivery is based on the ability to process information to reduce uncertainty and to clarify ambiguity (Daft & Lengel, 1984). The conceptual nature of banking services is an enactment between bank personnel and bank customers. During this encounter, bank personnel and bank customer must process and exchange information to reduce uncertainty and clarify ambiguity. In a channel choice perspective we can distinguish services according to the information needs of the service encounter. Such a categorisation have to account for use of coordination mechanisms that influence when information is exchanged and how it is available in service operations. Methods of coordination include standardisation by rules or programs, hierarchy, joint planning and lateral or electronic communication (Tushman & Nadler, 1978). Mintzberg refined the standardisation mechanisms by distinguishing standardisation of work processes, output, skills and norms (Mintzberg, 1979). Coordination by standardisation is a relatively simple mechanism to reduce equivocality when the bank personnel is involved in the encounter, but is less controllable in a self-service context where customers may need guidance. 2 The arguments in this paragraph is drawing heavily upon this seminal work in media richness theory. 122

5 Channel characteristics Media richness theory presumes that different media have different capabilities in information exchange and can be rated on a continuum between high and low media richness (Daft & Lengel, 1984). Applied to channels for bank services, customers can choose channels that do or do not match the information needs in a service encounter. In addition to this rational choice view on channel choice there are other factors that enable and/or motivate selection and use of a particular channel. Computer access at home or at work is a requirement for access to bank services on the Internet. US studies (e.g. Hoffmann & Novak, 1998) indicate that Internet adoption is explained by computer access. We can therefore conclude that those who have computer access are highly likely to have access to the Internet as a channel for banking services. Access to a channel is a prerequisite for it s adoption. Normally, adoption of banking services and channels is made by contractual agreements to obtain access to services and actual use of the channel. Accessibility in a channel choice situation is also related to a channel s availability in a particular service encounter. For example are Internet access restricted to situations where the customer have access to a computer with an Internet connection and the security means 3 that authorise a customer for these services. Accessibility may also be perceived differently when the computer is switched off compared to when it is up and running and connected to the Internet. For the branch office channel, accessibility include distance, opening hours and transportation effort. 4 In media choice theory other factors of importance to media richness include cue variety, feedback and personalization (e.g. Daft & Lengel, 1984; Zmud et al., 1990). Cue variety refers to the media s capability to attach multiple cues (e.g. wording, tone of voice and body language) to message (Zmud et al., 1990). Transferred to banking services and channel choice this can be interpreted as a channel's capability to combine information from various sources and at different moments in time during a service encounter. Feedback is a media s ability to clarify uncertainty and ambiguity during communication (Zmud et al., 1990). An important feedback mechanism for electronic channels is the degree of interactivity in the communication, i.e. more interactive media are richer than less interactive media. Personalization 5 of the channel and messages enables the customer to tailor the channel interface or the situation, e.g. by listing own contacts, enabling customised portals, or enabling customised transaction documents. Experiences from media choice research have resulted in inconclusive results for new media while the theory stick to more traditional media (e.g. Markus, 1994). It has been argued that 3 Be security means we refer to security code cards, digital code generators or small software components for authentication and encryption/decryption. 4 Differences in access to transportation means (car ownership and availability of public transport) can lead to differences in transportation effort for two customers with the same distance to a branch office. 5 Message personalization in media choice theory 123

6 new media do not fit into a media richness scale as the messages in new media may take different forms with different richness in the exchange. This form the basis for research on communication genres in electronic communication (Yates & Orlikowski, 1992) and the channel expansion theory (Carlson & Zmud, 1999). Carlson and Zmud found that experiences with the channel, communication partners and messaging topic were related to channel choice, although these results were in part ambiguous. Channel perceptions were used in an explanation of these result in a way that fitted the channel expansion theory in particular social influence (e.g. Fulk & DeSanctis, 1995). Additional explanation to channel adoption and use is found in the innovation literature (Moore & Benbasat, 1991; Rogers, 1983) and include relative advantage and compatibility. RESEARCH MODEL AND HYPOTHESIS The research model is based on the literature review in the background section, but is adjusted for data availability. The dependent variables are channel adoption and channel use. Individual differences is an independent variable. Demographic data are includes to the degree they are available in the bank systems, while personality traits is not. Data on relationship characteristics as economic activity and length of relationship is not. Demographics available includes age, sex and level of financial assets while data on income, education level is unavailable. The primary focus in this research paper is bank customer channel choice. Based on the literature review and an initial study of we ended with a focus on private customers and payment services in this paper. A study of channel choice depend on a service situation where customers have a channel choice. Payments is a situation that can be performed in many channels (internet, branch office, giro services, automated teller machines (ATM) and phone bank services) and where the bank customer makes the channel choice decision. Receiving payments is a situation where the bank customer does not have a choice, the channel choice decision was made by the paying customer. 124

7 Individual differences Age Sex Level of financial assets Channel adoption Relationship characteristics Economic activity Lenght of relationship Channel use Number of transactions Sum of transactions Figure 1: Research model, channel choice at customer level, private customers Based on the discussions of prior research on computer and internet access we have formulated the following hypotheses regarding the effect of demographic variables on channel adoption. Hypotheses about adoption rate There is a general and often taken for granted assumption that self-service banking in general and Internet banking in particular is adopted by young people. On the basis of these arguments, hypothesis 1 was formulated. H1 There are a negative association between age in years and adoption of self-service channels. Other studies (Kennickel & Kwast, 1997) did not find general support for this general association, but suggested and found support for a threshold value at 35. The alternative hypothesis 1.1 was therefore formulated. H1.1 Persons under 35 years of age are more likely to adopt self-service channels than persons older than 35 years. Examinations of data on Internet adoption in Sweden (Holst, 1999) indicated the possibilitt of a considerably higher threshold value, and the alternative hypothesis 1.2 was formulated. H1.2 Persons under 65 years of age are more likely to adopt self-service channels than persons older than 65 years. Internet have by many been regarded as primarily a male phenomenon, and if that should be true it would also affect the adoption of Internet banking. This is supported by Swedish data on Internet adoption (Holst, 1999) and data on adoption of Internet banking (Holst, 1999; SIFO, 2000), and hypothesis 2 was formulated. 125

8 H2 Men are more likely to adopt self-service channels than women. Kennickel and Kwast (1997) found an association between level of financial assets and the adoption of electronic banking. Hypothesis 3 was therefore formulated. H3 Persons with a high level of financial assets are more likely to adopt self-service channels than persons with a lower level of financial assets. As income and level of financial assets have been found to explain adoption and use of electronic banking we would also like to examine if level of economic activity is any predictor for adoption of self-service channels, and hypothesis 4 was formulated. H4 Persons with a larger economic activity are more likely to adopt self-service channels than persons with lower economic activity. Some studies by market research analysts indicate that the typical Internet banking customer is less loyal than other customers are. Internet customers should therefore be over-represented in the mobile customer mass. Therefore, customers with a short relationship to the bank is expected to be more Internet prone than customer with a long relationship to the bank, and hypothesis 5 is formulated. H5 Persons with a short relationship to the bank are more likely to adopt self-service channels than persons with a long relationship. Hypotheses about use A similar set of hypotheses were formulated for use: H6 There are a negative association between age in years and use of self-service channels. H6.1 Persons under 35 years of age are more likely to use self-service channels than persons older than 35 years. H6.2 Persons under 65 years of age are more likely to use self-service channels than persons older than 65 years H7 Men are more likely to use self-service channels than women. H8 Persons with a high level of financial assets are more likely to use self-service channels than persons with a lower level of financial assets. H9 Persons with a larger economic activity are more likely to use self-service channels than persons with lower economic activity. H10 Persons with a short relationship to the bank are more likely to use self-service channels than persons with a long relationship. 126

9 RESEARCH METHODOLOGY Measurements The study is based on raw transaction and customer data from the data warehouse of a Swedish bank. Scales were developed with the limitations of this data set. Dependent variables The dependent variables rate of adoption and use were developed on the basis of the raw data. The operational definition of adoption of a channel was at least one transaction in the channel during the month we had data on, and it was coded as a binary adoption variable per channel. Use was derived from transaction data by summarising transaction amounts pr. channel and number of transactions per channel. Independent variables The demographic data age, sex and level of financial assets were derive from the customer data base. Level of financial assets was measured in 4 dimensions; market value of funds deposits, total loans balance, total savings balance, and an aggregate customer balance that was the sum of these three dimensions. Measurements of economic activity were based on number of transactions per month and sum of transactions per month. Length of the relationship was measured in number of years since the customer joined the bank. Data collection For the final analysis we obtained customer and transaction data for March 2000 for the two branch offices Norrfors and Sydfors that participated in the research project. A common problem with raw data as a data collection method is data content and data quality. The discussion of prior research included variables and items that not are (or will be) found in customer and transaction databases. Therefore, the analysis was performed on a narrower set of variables than without these limitations. However, this collection method enable an analysis of data representing real customer behaviour and omits potential self-reporting biases (Collopy, 1996; Hufnagel & Conca, 1994) resulting in a high quality data set. There was, however, a need for major transformations of the data set in order to filter out a usable and reliable set of data. Sample characteristics A brief summary of the total raw database is listed in Table 1 below. These characteristics include all transaction in all channels. The data management procedures reduced these transactions to a number of payment transactions for private customers where the customer has an option to make a channel choice. 127

10 Table 1: Description of the data set (prior transformations) Norrfors Sydfors Total Number of private customers Business customers No. of transactions We removed all transaction where the customer received money, transactions that were generated automatically by the bank system (e.g. interests and fees), transactions in channels used by business customers only, and finally all payments where the customer do not have a channel choice (e.g. automated loan payments), resulting in a total of transactions on customers older than 20 years of age. RESULTS The results are presented in separate subsections for each of the dependent variables in our research model. Results on rate of adoption of self-service channels The analyses for the hypotheses on adoption of self-service channels (H1-H5) tested effects on rate of adoption variables per channel. The self-service channels for transactions in this study includes the use of ATMs, Internet, phone banking, card purchases, and the giro services (ordinary giro payment and automatic giro payment). Adoption was measured as a binary variable per channel and customer, where adoption was defined as having one or more transaction in a channel per month. Hypothesis 1 The relationship between age and adoption of self-service channels The relationship between age and adoption of self-service channels were tested by Chi-square tests of rate of adoption. No support was found for a relationship between age and adoption for any of the self-service channels, except the adoption of phone bank services (p=0.024). This result is explained by a lower rate of adoption for customers under the age of 18 (the adoption rate of this service was 6.7% compared to an adoption rate of 8.7% for the total sample). The alternative hypothesis 1.1 suggested a threshold effect in rate of adoption of self-service channels for customers that are 35 or younger versus older customers. There was no significant result for any self-service channel for these groups. Tests of hypothesis 1.2 suggested a threshold effect over and under the age of 65 yielded the same result, except for Internet banking (p=0.082) were the adoption rate were 12.8% for customer under 65 versus 11.2% for customers that are 66 and older. The large sample size (n=4604) have the effect that even small differences between groups may be statistical significant without having any practical significance. The conclusion is therefore that H1, H1.1 and H1.2 are strongly rejected, as only two of a large number of tests yielded statistical significant results. 128

11 Hypothesis 2: The relationship between sex and adoption of self-service channels The relationship between sex and adoption of self-service channels were tested by Chi-square tests of adoption. No support was found for a relationship between age and adoption for any of the self-service channels, except phone service (p=0.05, adoption rate 9.9% for women and 8.4% for men) and giro payment (p=0.015, adoption rate 45.1% for women and 41.8% for men). Hypothesis 2 was therefore strongly rejected Hypothesis 3: The relationship between level of financial assets and adoption of self-service channels Oneway ANOVA analysis was employed to test the difference between level of financial assets (measured in terms of customer balance, market value of funds deposits, total savings, and total loans) and the rate of adoption of self-service channels. Market value of funds deposits had a positive discriminating effect on Internet adoption (p=0.071), and total loans balance on automated giro payment adoption (p=0.065). No other significant results were yielded. As Internet is of focal self-service channel of this study, the conclusion is that H3 obtains some support. Hypothesis 4: The relationship between economic activity and adoption of self-service channels Hypothesis 4 was tested using oneway ANOVA. Economic activity was measured in terms of total sum of transaction and total number of transactions per month. Number of transactions was significant related to adoption for any self-service channel (p<0.01), while sum of transactions was significant for ATM (p<0.01), Internet (p<0.01) and giro payment (p=0.038). Hypothesis 4 obtained strong support. Hypothesis 5: The relationship between length of relationship and adoption of self-service channels Hypothesis 5 was tested by oneway ANOVA for all self-service channels. Length of relationship was measured by how many years the customer had been with the bank. There was no significant association for any self-channel and H5 was rejected. Results on use of self-service channels The analyses for the hypotheses on use of self-service channels (H6-H10) tested effects on degree of use variables per channel. The self-service channels for transactions in this study includes the use of ATMs, Internet, phone banking, card purchases, and the giro services (ordinary giro payment and automatic giro payment). Use was measured per channel and customer in terms of total sum of transactions and total number of transactions. Hypothesis 6 The relationship between age and use of self-service channels Oneway ANOVA was used to test H6, H6.1 and H6.2. The tests for H6 were significant for number of Internet transactions (p=0.036) but not for any of the other channels. The tests on H6.1 resulted in significant associations for sum ordinary giro transactions (p=0.045) and number of Internet transactions (p=0.029). There were no significant results for the tests on 129

12 H6.2. As the Internet is the focal self-service channel the conclusion is that H6 and H6.1 received support while H6.2 was rejected. Hypothesis 7: The relationship between sex and use of self-service channels Oneway ANOVA was used to test H7. For ATM transactions the results women had a significant (p=0.049) higher sum of transactions (mean 4685 SEK versus 4411 SEK for men) and number of transactions (p=0.043; mean 5.75 versus 5.42 for men). These differences are viewed as having statistical but not practical significance. H7 is therefore rejected. Hypothesis 8: The relationship between level of financial assets and use of self-service channels H8 was tested with correlation analysis pr. channel. There were no significant correlation between any variable for level of financial assets and any variable for use. H8 was therefore rejected. Hypothesis 9: The relationship between economic activity and use of self-service channels H9 was tested with correlation analysis per channel. There were significant correlations (p<0.01) between sum of transactions for most variables for channel use, except for sum ATM transactions (p=0.037), while there was no significant correlation between sum of transactions and number of ATM transactions. Number of transactions had significant correlations (p<0.01) with every variable for use except for sum phone transactions (p=0.05), while there was no significant correlation between number of transactions and sum Internet transactions. H9 was therefore accepted. Hypothesis 10: The relationship between length of relationship and use of self-service channels H10 was tested with correlation analysis per channel. No significant correlations were found, and H10 was rejected. DISCUSSION The results are discussed in separate subsection per dependent variable. Adoption of self-service channels The results of the analysis of the hypotheses H1-H5 are summarised in Table 2. Age has been suggested as an important antecedent of adoption of new technology in many studies. This study produced the contrary evidence and some of the analyses were produced significant indicators that rate of adoption is equal over all age groups. This result can be expected for channels that have reached the "majority" stage of channel adoption. This conclusion indicates that Internet banking is a mature channel, in terms of customer adoption behaviour. This is supported by the findings of differences in adoption rate of phone bank services between the youngest customer, who supposedly chose Internet banking rather than phone banking, and other customer who probably adopted phone banking before the Internet channel was available. A significant, but small difference in adoption rate of Internet banking above 130

13 and under 65 years is consistent with indications of a threshold value in Internet adoption discussed in the background section. There is no evidence of a digital divide based on sex, rather the results indicate strongly that there is no such difference. This result is in line with recent reports in the US that the gender gap is closing. 6 These results should, however, be interpreted with caution. Adoption and use of self-service banking may well be a household phenomenon and not a result of individual behaviour. If that is a common case, our data capture is linked to the person the service is registered on, not which member of the household that is actually carrying out the service. In line with other studies hypothesis 3 received significant support for market value of funds deposits, indicating that customer with a high level of financial assets are more likely to use self-service channels than customer with a lower level of assets. Other indicators of wealth, like total savings balance, did not have any effect. Further analysis has to be performed in order to find any threshold effects. However, this result may be explained by a high correlation with level of economic activity that received strong support. The results for this, almost tautological relationship is particularly interesting since it received strong support for any channel. Further analysis has to be carried out before it can be concluded if this relationship is merely tautological or if it also discriminated between channel adoption. Hypothesis 5 were rejected as there were no evidence of any difference in adoption of selfservice channels based on how many years the customer had been with the bank. This result is consistent with the suggestion that the self-service channels in this study are in mature stage of adoption. Table 2: Summary of results on self-service adoption Hypothesis Result Comment H1: Age on adoption Reject H1.1 Age on adoption, threshold on 35 Reject H1.2 Age on adoption, threshold on 65 Reject H2 Sex on adoption Reject Minor differences for phone service and ordinary giro H3 Level of financial assets on adoption H4 Level of economic activity on adoption H5 Relationship length on adoption Support Strong support Reject service Supported for market value of funds deposit on Internet adoption It may therefore be concluded that the rate of adoption of Internet banking is best explained by level of financial assets measured as market value of funds deposits. The support for H4 is not useful as it did not discriminate between channels. 6 The source of these reports are articles in technology newsletter (e.g. Edupage) that quote polls performed by market research firms. 131

14 Use of self-service channels The results of the analysis of the hypotheses H6-H10 are summarised in Table 3. While age did not have any effect on adoption it had significant effects on the level of use of Internet transactions, suggesting that younger customers have more transaction in the Internet channel than older customers. The hypothesised threshold effect for customer over and under 35 years also gained support, while there was no support for the hypothesis of a threshold effect over and under 65. We did not find any differences explained by sex that had practical significance, and H7 was rejected. This is consistent with recent reports of a closing gender gap in adoption and use of Internet from US market research firms. Level of financial assets had significant effect on the adoption of self-service channels, but did not discriminate between high and low level of use for those who adopted self-service. Economic activity, measured as number of transactions and sum of transaction in a month, had significant effects on level of use of self-service channels. The results were significant for any channel and did not reveal any differences in channel choice. An additional test for branch office transactions had similar results. Without any discriminating effect between channels, this relationship becomes tautological, as people with many transactions have more transaction in all channels than customer with fewer transactions. An identical relationship was found for sum of transactions. The conclusion is that H9 got strong support, but the interpretation is meaningless. The hypothesised relationship between the length of the relationship with the bank on use of self-service channels did not yield any significant results, and consistent with the argument of self-service channels maturity. Table 3: Summary of the results on self-service use Hypothesis Result Comment H6: Age on use Support H6.1 Age on use, threshold on 35 Support H6.2 Age on use, threshold on 65 Reject H7 Sex on use Reject H8 Level of financial assets on use Reject H9 Level of economic activity on use Strong support H10 Relationship length on use Reject It is therefore concluded that age has a small but significant effect on level of use of Internet banking measured as number of transactions. The support for H9 is not useful as it did not discriminate between channels. CONCLUSIONS The results of this study indicate that current self-service channels for payments are in a mature stage of adoption and use. From this we can not conclude that they are mature as a business concept, on the contrary it will be suggested elsewhere that current self-services exploit only a fraction of the business opportunities enabled by these channels. 132

15 Therefore, traditional individual differences, such as age and sex, which were expected to discriminate between adopters and non-adopters, did not influence rate of adoption. On the other hand, a small but significant difference in level of use was found between younger and older customers. This study gave some answers that were contradictory to previous research, but consistent with recent findings in US market research. With the potential disadvantage of a large sample size that may result in an overfit of data, one could expect support for most hypothesis, as even small differences between groups will be significant. However, sample size turned out to be strength of this study as only a few hypotheses gained support. It can therefore, with a high level of confidence, be concluded that there is no differences in rate of adoption between groups based on age, sex or length of relationship for self-service channels in contexts similar the one of this study. It can also, with the same level of confidence, be concluded that there is no association between sex, level of financial assets, and length of relationship, and level of use of self-service channels. Finally, level of economic activity is not useful as predictor of channel preferences. REFERENCES Bitner, M. J., Brown, S. W., & Meuter, M. L. (2000). Technology infusion in service encounters. Journal of the Academy of Marketing Science, 28(1), Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, W. B. G. (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in need for cognition. Psychological Bulletin, 119(2), Carlson, J. R., & Zmud, R. W. (1999). Channel expansion theory and the experiental nature of media richness perceptions. Academy of Management Journal, 42(2), Collopy, F. (1996). Biases in retrospective self-reports of time use: an empirical study of computer users. Management Science, 42(5), Dabholkar, P. A. (1996). Consumer evaluations of new technology-based self-service options: an investigation of alternative models of service quality. International Journal of Research in Marketing, 13(1), Daft, R. L., & Lengel, R. H. (1984). Information Richness: A New Approach to Managerial Behavior and Organization Design. Research in Organizational Behavior, 6, Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA.: Addison-Wesley. Forsyth, J., Gupta, S., Haldar, S., Kaul, A., & Kettle, K. (1999). A segmentation you can act on. McKinsey Quarterly(3), Fulk, J., & DeSanctis, G. (1995). Electronic communication and changing organizational forms. Organization Science, 6(4), Goodhue, D. L., & Thompson, R. L. (1995). Task-Technology Fit and Individual Performance. MIS Quarterly, 19(2), Hoffmann, D. L., & Novak, T. P. (1998). Bridging the racial digital divide on the Internet. Science, 280(5362 (17 April)). Holst, M.-G. r. (1999). TELDOKs Årsbok 2000 (Teldok Rapport 130): TELDOK. 133

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