Customer preferred channel attributes in multi‐channel electronic banking

Customer preferred channelattributes in multi-channel

electronic bankingTommi Laukkanen

Department of Business and Management,University of Kuopio, Kuopio, Finland

Abstract

Purpose – The purpose of the study is to increase the understanding of the diverse retail channelpreferences of online bank customers by examining their channel attribute preferences in electronicbill paying. Two different groups of online customers were examined: those who pay their bills overthe internet and those who, in addition, have experience of using a mobile phone for this service.

Design/methodology/approach – A large internet survey was implemented and conjoint analysiswas used in order to identify the utilities of the attribute levels and relative importance of the differentattributes. Moreover, cluster analysis was used to group the individuals into homogenous attributepreference segments.

Findings – The empirical findings indicate that internet users and mobile users differ in theirchannel attribute preferences. The results suggest coherent customer preference segments in bothgroups. In addition, the study identifies a group of potential mobile banking users among those whohave never used a mobile phone for banking actions.

Research limitations/implications – An internet survey design exposes the study to somelimitations. All the respondents were online users of one bank, which implies problems withgeneralising the results to the whole population. Moreover, the questionnaire was open only for alimited time period, exposing the study to a potential bias.

Originality/value – The study provides new insights for decision makers in the electronic retailsector and especially in banking. The results contribute information for banks’ marketing actions andprovide indications to device producers of the need for a more diversified supply of devices for diverseconsumer groups.

Keywords Consumer behaviour, Internet, Mobile communication systems, Virtual banking, Banks,Finland

Paper type Research paper

IntroductionAs electronic distribution channels become increasingly sophisticated, serviceproviders are providing multi-channel services for their customers. It is argued thatthe entire environment in retailing is being changed with the introduction ofmulti-channel systems designed to offer a range of retail experiences and differentkinds of value for customers (Mathwick et al., 2001). However, Schoenbachler andGordon (2002) note that the focus of development of multi-channel service distributionhas mostly been on the channels: how to improve the channels, or how to directcustomers to the channels without violating the existing customer relationships.However, they argue that the focus should be on consumer preferences instead ofchannels, and suggest that a customer-centric focus would enable managers to betterdevelop and design successful and effective distribution channels.

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0959-0552.htm

Customerpreferredattributes

393

Received October 2005Revised June 2006

Accepted July 2006

International Journal of Retail &Distribution Management

Vol. 35 No. 5, 2007pp. 393-412

q Emerald Group Publishing Limited0959-0552

DOI 10.1108/09590550710743744

As a technical platform for service delivery, different electronic channels havedifferent characteristics. These characteristics form the basis for the creation ofcustomer perceived value. Thus, the means to provide value for customers differbetween channels. Earlier studies have suggested that consumers prefer a mix of severalchannels rather than a single channel in banking (Howcroft et al., 2002). Furthermore,some customers seem to want increasing access to all available delivery channels and donot regard them as substitutes (Durkin et al., 2003). In order to manage the supply indifferent distribution channels efficiently, service providers are faced with a need tounderstand what services to deliver in which channel. Moreover, in order to focus theirmarketing actions, decision makers need to understand how consumer preferences differbetween different channel characteristics in the consumption of electronic services.

The aim of this study is to explore consumer preferences regarding attributes ofelectronic channels in bill payment services among Finnish bank customers. Moreover,consumer segments with different preferences are explored. A large quantitativeinternet survey was implemented. The first section of the paper briefly outlines thepurpose of the study and describes the banking industry in Finland. This is followedby a discussion on customer perceived value and its influence in attribute preferences.Third, an overview of the method used is provided and the implementation of the studyis described. Subsequently, the results of the study are presented and finallyconclusions and suggestions for future research are made.

Purpose of the studyThis study was conducted in order to increase the understanding of diverse retailchannel preferences among online bank customers. The primary objective of the paperis to examine customers’ attribute preferences in electronic distribution channels in billpayment service. The preferences of two different customer groups are investigated:those who pay their bills over the internet using personal computers and those who,in addition, have experience of using mobile bill paying. Earlier studies in internetbanking have reported that customers prefer this mode of bill payment due to thelow fees, time savings and freedom from time and place (Karjaluoto et al., 2002).Other factors contributing the use of internet banking include easiness-to-use of theservice and speed of service delivery (Karjaluoto, 2002) as well as convenience andcompatibility with lifestyle (Black et al., 2002; Gerrard and Cunningham, 2003). Indeed,there has been a dramatic growth in the adoption of internet banking world-wide.In Finland, the usage rate is one of the highest in the world. Already, 75 per cent ofthe Finns use the internet at home, in the workplace or elsewhere and as many as66 per cent of the age group 15-74 years regularly use banking services via internet(The Finnish Bankers’ Association, 2005).

It is assumed that the mobile phone as a channel for service consumption wouldoffer enormous potential in banking, since today a mobile phone is an integral part ofcustomers’ lives and a growing number of these devices are also equipped withinternet connection (Laukkanen and Lauronen, 2005). Previous studies indicate thatfactors contributing to the adoption of mobile banking are related to convenience,access to the service regardless of time and place, privacy and savings in time andeffort (Suoranta, 2003). Furthermore, it is argued that the use of mobile bankingservices would increase one’s self-prestige (Lee et al., 2003). The major advantage of amobile phone for service consumption is its portability and access to the service

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whenever and wherever wanted. In mobile-bill payment, especially, the location-freeaccess to the service and as a consequence the ability to react immediately to theservice need, to use the service wherever wanted and to save time have been found tobe the greatest contributors of the service (Laukkanen and Lauronen, 2005).However, the consumption of banking services via mobile phone has remained slow.The mobility restricts the technical features of the device and impairs the usability ofthe service. Tapping information using small keyboards of cell phones and informationprocessing on a small display have been found to be inconvenient and to inhibitmobile-bill payment (Laukkanen and Lauronen, 2005). Therefore, the aim of the studyis to explore how customers value different channel attributes in bill paying.

Customer perceived valueThe creation of superior customer value has been one of the prime interests ofindustrial marketing research. At the same time, academia has been interested inexploring how customers perceive value. A clear distinction should be made betweenvalues and perceived value. In which, value is seen as an enduring belief that guideshuman behaviour (Vinson et al., 1977), perceived value is generally seen as a trade-offbetween benefit and sacrifice (Zeithaml, 1988). Flint et al. (1997) argue that whereasvalues represent abstract, centrally held higher order goals which are independent ofuse situation, the perceived value is an overall view of trade-offs between benefits andsacrifices which is dependent on specific use experience. However, it is argued thatviewing perceived value as a trade-off between quality and price is too simplistic(Bolton and Drew, 1991; Sweeney and Soutar, 2001). These views suggest that existingvalue constructs are too narrow and that other dimensions would increase theconstruct’s usefulness. Holbrook (1994, 1999, pp. 5, 27) suggests a more detaileddefinition for customer perceived value as an “interactive relativistic preferenceexperience”. Typically such customer value refers to an evaluation of some object bysome subject and value depends on the attributes of some physical or mental object,but cannot materialise without the involvement of some subject who appreciates thesecharacteristics (Frondizi, 1971; Holbrook, 1994, 1999). The subject is usually seen as acustomer, whereas the object is generally defined as a product or service. Byinteractive, Holbrook refers to interaction between the object and subject, whilerelativistic means that the perceived value is comparative, personal and situational.However, most effort in the definition is put into the preference experience thatsubsumes a preference judgement and preference order. It is argued that in bankingservices, for example, the use of various channels to deliver the same servicedrastically affects the nature of the service experience for the customer (Lovelock andWirtz, 2004). In other words, based on usage experience a customer prefers someattributes of a physical or mental object over others. Accordingly, Lancaster (1966)considers consumption as an activity in which goods, singly or in combination, areinputs and in which the output is a collection of attributes. He argues that consumerpreference orderings rank collections of attributes and only indirectly rank collectionsof goods through their attributes. He means that goods per se do not create value forconsumers, but rather the attributes of the goods create value for consumers.Therefore, it is hypothesised in this study that:

H1. The channel attribute preferences for bill payment differ between users andnon-users of mobile banking.


395

Conjoint analysisThe starting point for marketing as a discipline lies in an adequate analysis of consumerpreferences for specific products or services in which a conjoint analysis is seen as ageneral model (Vriens, 1999). This is a generic term coined by Green and Srinivasan (1978)to refer to a number of paradigms in psychology, economics and marketing that areconcerned with the quantitative description of consumer preferences and value trade-offs(Louviere, 1994). It is a method providing utility estimates of different product or serviceattributes by having the respondents evaluate different levels of the attributes. In theirsurvey of research suppliers in the USA, Wittink and Cattin (1989) documented a greatnumber of conjoint applications in a wide variety of industries. Indeed, since itsintroduction into marketing conjoint analysis has developed into a method for preferencestudies meriting attention from both academics and practitioners and has become one ofthe most widely applied marketing research methods for predicting and understandingconsumer and customer trade-offs, decisions and choices (Louviere, 1994).

Conjoint analysis also allows for responses to be segmented based on respondents’preference structure. Indeed, market segmentation in commercial applications is foundto be one of the primary reasons for undertaking conjoint analysis (Wittink and Cattin,1989; Wittink et al., 1994). In this case, the method assumes that the customer evaluatesan electronic distribution channel as a bundle of attributes. The results providea quantitative approximation (utility) of how each level of attribute influencesthe decision, and show the relative contribution, in percentages, of each feature to thedecision process overall. The following seven-step process for conducting the conjointstudy was applied (Gustafsson et al., 2003):

(1) selection of the preference function;

(2) selection of the data collection method;

(3) selection of the data collection design;

(4) selection of the way the stimuli are presented;

(5) selection of the data collection procedure;

(6) selection of the method for evaluating the stimuli; and

(7) estimation of the benefit values.

MethodologyAccording to Louviere (1994), it is important that conjoint analysts first decide whichattributes influence consumer decisions in a particular research context. He argues thatsuch attributes are usually identified from existing academic literature or exploratoryresearch. However, it is critical to have a carefully thought out list of attributes since anincreased number of parameters to be estimated requires either a reduction in thereliability of parameters or a larger number of stimuli (Hair et al., 1998). Furthermore, alist of too many attributes can greatly increase the burden on respondents, but a listwith too few attributes may reduce the predictive capabilities of the research design.Therefore, in this study, an exploratory pilot study was conducted with 20 experiencedinternet and mobile-banking customers. As suggested by Huber et al. (2001), themeans-end technique was used in order to identify the most relevant attributes for theconjoint study. The pilot study showed that screen size, keyboard, service accesslocation and response time are the most important value driving attributes in internetand mobile banking (Laukkanen and Lauronen, 2005; Laukkanen, 2006). Insight into

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the selection of attributes was also gained from earlier electronic banking studies(Heinonen, 2004; Pousttchi and Schurig, 2004). Table I describes the attributes andattribute levels used in the conjoint study.

Selection of the preference functionThe first step in the conjoint analysis is to make assumptions regarding therelationships of the levels within an attribute (Gustafsson et al., 2003). This means thatthe researcher should assume how individual attribute levels impact on the importanceof the attribute. In this study, the attribute levels were considered discrete but includethe assumption that bigger screen size and keyboard would be preferable againstsmaller and more flexible location would be preferable against more fixed locations.Moreover, a shorter response time was considered preferable against a longer responsetime. However, the preference function was not considered as linear in any attributelevel. The following hypotheses are made:

H2. A bigger screen size is preferred over a smaller screen size.

H3. A bigger keyboard is preferred over a smaller keyboard.

H4. Flexible location is preferred over more fixed locations.

H5. Shorter response time is preferred over longer response time.

Selection of the data collection method and designThere are two classic data collection methods for conjoint analysis:two-factor-at-a-time procedure and a full-profile approach (Green and Srinivasan,1978). The two-factor-at-a-time procedure, also known as a trade-off procedure,compares two attributes at a time by ranking all combinations of levels. It is arguedthat the method lacks realism and may involve too many judgments to be made by therespondents (Green and Srinivasan, 1978; Green, 1984) and therefore, its use hasdecreased dramatically in recent years (Hair et al., 1998).

The full-profile approach is the most frequently used presentation method in conjointanalysis (Hair et al., 1998). It represents real buying situations because it utilises thecomplete set of attributes and their levels. The basic idea is to form profile cards,evaluated by the respondents, including all the possible combinations of attributes

Attribute Attribute level

Screen size About 2 inches (e.g. cell phone)About 5 inches (e.g. Nokia Communicator)About 13-19 inches (e.g. PC)

Keyboard Small (e.g. cell phone)Medium (e.g. Nokia Communicator)Normal PC-keyboard

Location Fixed (e.g. library)Home or workFlexible

Response time 0-3 seconds3-10 secondsOver 10 seconds

Table I.Attributes and attribute

levels


397

and their levels. However, a major limitation of the approach is recognised, since as thenumber of attributes increases the possibility of information overload also increases(Hair et al., 1998). The information overload may result in a temptation for therespondent to simplify the experimental task or the attribute levels themselves (Greenand Srinivasan, 1978) and may therefore reduce the accuracy of respondents’ preferenceevaluation. However, the number of profiles the respondents need to evaluate can bereduced by using fractional factorial design, which is also called orthogonal design inSPSS. It is argued that respondents often prefer the reduced design, which attempts torepresent the complete design based on a smaller number of profiles (Green andSrinivasan, 1978). In this case, the full-profile method would have included 81(3 £ 3 £ 3 £ 3) possible combinations which would have yielded an excessiverespondent burden and jeopardised the quality of the responses. By using the fractionalfactorial design, the combinations could be reduced to nine profiles.

Selection of the way the stimuli are presentedTypically, the profiles are presented in verbal form. Since, verbal labels are not alwaysoptimal in presenting the meaning of some attribute levels, visual representationmethods have sometimes been used (American Marketing Association, 2000). Greenand Srinivasan (1978) found several advantages of visual profiles over verbal profiles.They argue that information overload is reduced, higher homogeneity of perceptionsacross respondents is achieved, the task is perceived to be more interesting, and thestimuli are perceived to be more realistic. Vriens et al. (1998) provide a detailedcomparison between verbal and pictorial representations in conjoint analysis. Theyargue that pictorial representations improve respondents’ understanding of the designattributes, while the verbal representations facilitate respondents’ judgment. In thisstudy, a combination of pictorial and verbal representations was used. Whereas,location and response time were represented verbally screen size and keyboard wererepresented both verbally and pictorially in each profile card.

Selection of the data collection procedureConjoint studies, like many other surveys, can be conducted by telephone, face-to-faceinterview, mail, computer, or the internet. According to Wittink and Cattin (1989),conjoint studies are mainly conducted by telephone or mail to ensure geographicrepresentativeness. This conjoint study is conducted using the internet which hasincreased its popularity in recent years. Two questionnaires on online bill paymentwere designed: one for internet users and the other for those who also had experience ofusing a mobile phone for paying bills. The survey was designed to ensure that therespondents had recently used online banking services and therefore, the questionnairewas placed in the log-out page of a large Scandinavian bank’s online service in Finland.Altogether, 2,169 completed responses to the internet-bill payment questionnaire and81 to the mobile-bill payment questionnaire were collected.

Selection of the method for evaluation the stimuliRespondents’ evaluation and measurement of potential products takes either a metricscale, used for rating, or a non-metric scale used for ranking and paired profilescomparisons. When a rating scale is used, respondents normally grade perceivedbenefits on a numerical scale, but, when a ranking scale is used they only present

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a preference order (Gustafsson et al., 2003). Thus, when the ranking format asks therespondents to rank product or service profiles from most preferred to least preferred,the rating format allows respondents to indicate their preferences for the profiles interms of numbers. In this study, the rating method was used since the rank-orderingtask usually entails a personal interview due to the considerable amount of informationrequired by the research procedure (Green and Srinivasan, 1978). Moreover, ratingscales and metric measures are easily analysed and administered by various datacollection procedures like the internet (Hair et al., 1998). The respondents were asked torate the profiles by indicating how well on a scale 1-10 the fictional device describedwould serve the respondent for paying bills, a high score indicating greater likelihood.Figure 1 shows an example of a profile card.

Estimation of the benefit valuesGroup statistics were analysed at the aggregate level in order to find the utilities of theattribute levels and the relative importance of the different attributes. However, sincecustomer perceived value is defined as personal, there is a reason to believe thatattributes and their levels are weighted differently by customers. Cluster analysis can beemployed to group respondents with similar preferences (Green and Srinivasan, 1978).In this study, using K-Means clustering with individual-level results the individualswith similar responses were grouped together. K-Means clustering is currently the mostpopular partitioning method (Churchill, 1999) and its popularity is claimed to derivefrom fast execution and its ability to handle large data sets (Sambandam, 2003). Assuggested by Saunders (1994), analysis of variance (ANOVA) was used for testing thevalidity of the clustering. The discussion above leads to the following hypothesis.

H6. Channel attribute preferences differ significantly between individuals.

Figure 1.Example of a profile card

How well would the above fictional device serve you for paying bills?

Screen size: About 5" (e.g. Nokia Communicator)Keyboard: Normal PC keyboardLocation: Home or workResponse time: 0-3 sec

(1=not at all, 10=very well)

Previous Next

1 2 3 4 5 6 7 8 9 10


399

ResultsThe aggregate results indicate that for the internet users the most important attributeis screen size (36.3 per cent), followed by location (24.8 per cent) and response time(22.3 per cent). The customers who also use a mobile phone for paying bills, onthe other hand, pay the greatest attention to location (44.7 per cent), followed by screensize (23.9 per cent) and response time (22.3 per cent). The importance attachedto keyboard was relatively low in both internet and mobile groups being 16.6 and9.2 per cent, respectively, (Table II).

The results support H1, since the preferences of mobile-bill payment users differfrom those customers who do not use a mobile phone for bill payment. Taking a closerlook at each attribute separately, it is noticeable that in the internet group a decrease inthe screen size from 1300-1900 (PC screen) to 500 (like Nokia Communicator) results in areduction in utility of 1.4925 (1.2771 2 (20.2154)) whereas, in the mobile group thereduction is only 0.4279 (0.5706 2 0.1427). This indicates that usage experience has aninfluence on the perceived value of the device attributes. More importantly, it seemsthat in the mobile group a decrease from 5 to 200 results double reduction in utility(0.856) compared to the shift from 1300-1900 to 500. This indicates that among mobile usersa screen size of about 5 inches is considered adequate for bill paying whereas a 2-inchscreen is clearly far too small. Among those who do not use a mobile phone for payingbills, the large screen is considered the only appropriate option. Moreover, the resultssupported H2, since bigger screens were preferred over smaller ones in both groups.

It is notable that the results did not support H3, which suggested that a biggerkeyboard was preferred over smaller ones, but the results indicate that actually themidsize keyboard (as in Nokia Communicator) is the most preferred by the customers.Moreover, H4 is supported by the results, since more flexible service access pointswere preferred over more fixed locations. Fixed locations are totally unacceptable forboth groups, the utilities being 20.9148 for the internet group and 21.3842 for themobile group. However, a shift from “home or work” location to totally flexible locationresults in an increase in utility of 0.45 (0.6824 2 0.2324) in internet group and of 0.6625(1.0233 2 0.3608) in the mobile group indicating mobile users’ greater preferencetoward totally flexible location compared to internet users.

The biggest difference between these two groups of users relates to service-responsetime. Whereas, an increase in the response time from 0-3 to 3-10 seconds appears to havecaused hardly any reduction in utility (0.1028) among the mobile group the reduction isnotable (0.6694) among the internet group. However, over 10 seconds response time isconsidered unacceptable in both groups; the results indicate that mobile users are morepatient with the service. Probably, mobile users are used to being more forbearing inthe bill paying process whereas those who have not used a mobile phone for the serviceexpect instant interaction with the service.H5 is supported by the results in both groupssince customer preference decreases when response time becomes longer.

In order to test H6, the attribute variations are explored in more depth and thedifferences between homogenous groups obtained from cluster analysis are examined.The results suggest that at least five customer segments can be found among internetusers (Table III) and four among mobile users (Table IV), each with their ownparticular preferences regarding electronic channel attributes. The findings supportH6, since the analysis identified significantly distinct groups of internet and mobileusers based on their channel attribute preferences. The F statistics showed that the

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Table IV.Potential mobile banking

users


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Table IV.

IJRDM35,5

404

importance of a totally fixed service access point differentiated most between thegroups among both internet and mobile users. Moreover, the importance of a PC-sizedscreen and home/work access among internet users and totally flexible access amongmobile users seemed to divide opinions greatly.

Internet users’ Group 1, labelled as “Time conscious” formed a distinct group ofcustomers whose utility scores indicated perceived high benefits for response time.The relative importance of the attribute is 32.4 per cent in the segment. Moreover, theutility scores indicate that the difference in response time between 0-3 seconds (1.62)and 3-10 seconds (20.31) is crucial in this segment, showing a great decrease in utility(1.93) with a few seconds increase in response time. Furthermore, this segment has agreat interest in flexible connection, medium keyboard and large screen of the device.The second group, “Location oriented” was characterised by the great relativeimportance (61.5 per cent) they attached to the service access location. A fixed location,like a library or other public internet access, is totally unacceptable for this group butthere is hardly any difference in utility between home/work access (1.57) and totallyflexible access (1.59) to the service. In line with Group 1’s preferences, this group alsopreferred large screen, medium keyboard and fast access speed. Owing to the greatimportance that customers in Groups 1 and 2 attach to location and their preference formedium keyboard profiles them as potential mobile users in the near future when thetechnological development, like 3G, improves the connection speed of mobile devices.

Respondents in Group 3 were characterised by the importance they attached to thekeyboard of a device – whilst in this group the greatest utility was given to screen size,they had a preference, unlike other groups, for using a normal PC-keyboard. In contrastto Groups 1 and 2, it seems that the reason for Group 3 not to use any mobile devicesfor paying bills is the devices’ limited tapping and visual capabilities. Group 4, labelled“Visually oriented” put the greatest attention to the screen size of a device, the relativeimportance being 77.6 per cent. They strongly dislike the smaller screens used inmobile devices and prefer the screen sizes used in personal computers, the reduction inutility being 4.30 in the case of decrease in screen size from 1300-1900 to 500. Moreover, theutility figures indicate that a totally flexible connection (0.27) appears to interest themvery little compared to home or work access (0.26) the increase in utility being only(0.01). Even though Group 5 attached greatest importance to screen size, in contrast toother groups, they preferred home or work access over the most flexible connection.They seem to be very satisfied with paying their bills at home or work and feel no needfor more flexible service access method. The users of this group will hardly perceiveany added value in mobile banking in the foreseeable future.

Since, groups’ 1 and 2 channel attribute preferences revealed a potential for mobilebanking, it is interesting, from the managerial perspective in particular, to identify thesecustomers. In this respect, the potentiality reflects on the one hand the importance offlexibility of service access location and on the other hand the importance of screen sizeand keyboard in service consumption. The more important the flexibility and the lessimportant the screen and keyboard of the device are to the respondent in bill paying, themore likely he is considered to be to adopt mobile banking. Response time was not seenas an obstacle in the days to come when technological developments, like 3G, will mostlikely overcome this barrier. Thus, the potential mobile banking users’ backgroundvariables were compared with those with less potential. The results indicatethat education, household income, profession, internet-banking usage experience and


405

usage frequency distinguish potential mobile-banking users from other internet users(Table V) while age, gender, size of the household, province or place of residence had nostatistically significant influence.

Like the internet users, the mobile users’ channel attribute preferences for bill payingseem to vary a lot. Group 1 attached the greatest importance to the screen size of the device.They preferred the bigger screen sizes and keyboards normally used in computers. It isargued that, in fact, many customers prefer bigger screen size for mobile bill paying so thatthey can see the entire bill on the screen (Laukkanen and Lauronen, 2005). Moreover,Group 1 preferred service access from home or work and were satisfied with 3-10 secondsresponse time. It seems that this is a group of customers who use mobile phone only as anadditional channel for paying bills in exceptional circumstances. They clearly dislike thefeatures of the mobile phone in service consumption. These views contrasted with those ofcustomers in Group 2, who were more spatially and temporally oriented. This group ofcustomers preferred flexibility in service consumption and valued quick processing withthe service. It seems that they use the mobile phone for paying bills due to the locationconvenience the device offers. They attach great importance to a very high-speed mobileconnection since an increase in response time from 3-10 seconds (20.45) to 0-3 seconds(1.00) resulted in great increase (1.45) in utility. Customers in Group 3 resemble thecustomers in Group 2 in their relative preferences. However, the customers in Group 3attached hardly any importance to the screen size (3.8 per cent) or keyboard (11.9 per cent)of the device and not even the response time seems to matter to them. Almost, the onlyattribute they are interested in is location, the relative importance being 70.5 per cent.These bank customers seem to prefer a Nokia Communicator type of device for payingbills due to its mobility and, at least for them, optimal-sized screen and keyboard.

The customers in Group 4 paid greatest attention to connection speed, clearlyabhorring over 10 seconds response time but being satisfied with a connection speedproviding a response time of 3-10 seconds. Similarly, they disliked the smallest screensin mobile phones but an increase from 500 (0.28) to 13-1900 (0.37) appeared to interestthem relatively little.

Conclusions and practical implicationsService providers and product manufacturers are concerned with finding out whichproduct or service characteristics are most important to their customers. Products andservices are thus defined through a limited number of relevant characteristics or attributeswhich have a number of levels (Vriens, 1999). Conjoint analysis is a technique formeasuring how much customers prefer these attributes and their levels. Furthermore,customers’ preferences tend to differ from each other and therefore marketing managers inindustrialised countries cannot manage without segmenting the markets. With clusteranalysis, the customers with similar preferences can be grouped to identify segments.

The technical elements of the service outcome, the functional elements of the serviceprocess as well as time and location have been acknowledged as important characteristicsin customer value perceptions (Heinonen, 2004). The purpose of this study was to explorehow different channel attributes are valued by customers in internet and mobile billpaying. The results of the conjoint study suggest that internet and mobile users differ intheir preferences toward electronic channels’ attributes in bill paying. It seems that forinternet users the screen size, followed by the location and the response time are the mostimportant channel attributes in bill paying. For mobile users, however, the location,

IJRDM35,5

406

Att

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Table V.Mobile users’ attribute

preference segments


407

followed by the screen size and response time are the channel attributes of greatest interestwhen using the service. The cluster analyses showed that the attributes meet differentneeds from different customer segments. It is important to recognise these differences,since they provide the banks with a basis for developing their communication strategies.For example, the results indicate groups of potential mobile banking customers amongcurrent internet users encouraging banks to develop marketing actions to increase thenumber of mobile users. Moreover, the results show that in service consumption,especially, consumers’ channel attribute preferences vary indicating a need for a morediversified supply of devices for different consumer groups. It seems that enhancedconnection speed and bigger screens in mobile devices will increase the number of mobilebanking users and even result in new user segments in the near future.

Limitations of the study and suggestions for future researchSome limitations are evident in the study. Owing to the focus of the study only online usersparticipated, causing problems with generalising the results to the whole population.Moreover, the questionnaire was placed on the log-out page of only one bank’s web servicewhose customers may differ behaviourally from customers of other banks, causingproblems with generalising the results to other banks’ customers. In addition, responserates are difficult to determine accurately since in internet surveys the invitation may beseen several times by some and totally missed by others. However, this likelihood wasreduced by arranging in advance for the questionnaire to open up only for every fifthvisitor and by limiting the period of the survey to 48 hours. On the other hand, sincedifferent people may bank online on weekdays than at weekends, placing the survey onthe banking site for 48 hours only exposed the study to a potential bias.

Even though cluster analysis is widely used and applied in multiple research settings, ithas some limitations that should be taken into consideration. Cluster analysis is a way ofsorting items into homogenous groups (Saunders, 1994). The fundamental idea of clusteranalysis is that the boundaries of the groups are not specified in advance, but instead arederived according to patterns found in the measurement of the items (Arnold, 1979). Anumber of authors have viewed the technique with some scepticism (Saunders, 1994).Some have been concerned about determining the appropriate number of clusters ormeasures of similarity (Green et al., 1967; Frank and Green, 1968) while some haveexpressed reservations about the clarity or sharpness of the homogenous clusters (Wells,1975). A problem with the method is that cluster analysis partitions the entities whether ornot there are natural groupings (Arnold, 1979) and therefore, there could be a wide gapbetween the intention of the researcher and what happens in reality (Sambandam, 2003).One way to deal with the problem is to analyse the correlations between the variablesmeasured, since collinearity may cause the researcher to come up with unreal results(Sambandam, 2003). In this study, the correlations were measured between the 12 channelattribute levels separately in the internet and mobile data (Appendices 1 and 2).Sambandam (2003) suggested that, in the context of cluster analysis, a correlation valueabove 0.5 indicates a high correlation and a correlation value below 0.2 indicates a lowcorrelation. No high correlations were found between the levels of different attributes.The only high correlations observed were between the levels of each attribute. Theseintra-attribute correlations were markedly negative, which is a logical outcome of conjointanalysis measuring customer preferences for attributes and their levels. For example,if a customer prefers totally flexible service access location, he will scarcely give high

IJRDM35,5

408

scores to fixed locations. Indeed, without the negative intra-attribute correlations, thevalidity of the conjoint study would become questionable.

The absence of high correlations between the levels of different attributes indicatesbelievable and real clusters. However, it needs to be taken into consideration that thehighly negative intra-attribute correlations may have had an influence on the partitioning.

The aim of this study was to explore two different customer groups’ channel attributepreferences namely those of internet and mobile users in the consumption of one service.Future research could focus on customers’ channel attribute preferences in differentelectronic services, since if the preferences differ between the users of different channels,it may well be that the preferences also differed in the consumption of different services.

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Appendix 1

About2 inches

About5 inches

About13-19 inches Small Medium

NormalPC-keyboard

About 2 inches 1 20.081 20.677 0.171 20.073 20.107About 5 inches 20.081 1 20.679 0.065 0.068 20.122About 13-19 inches 20.677 20.679 1 20.174 0.004 0.168Small 0.171 0.065 20.174 1 20.412 20.633Medium 20.073 0.068 0.004 20.412 1 20.445Normal PC-keyboard 20.107 20.122 0.168 20.633 20.445 1Fixed 20.270 20.199 0.346 20.199 20.062 0.249Home or work 0.215 0.251 20.343 0.274 20.052 20.225Flexible 0.153 0.009 20.119 20.017 0.149 20.1100-3 seconds 20.028 0.199 20.126 20.247 0.316 20.0263-10 seconds 20.133 0.034 0.073 20.005 20.246 0.215Over 10 seconds 0.142 20.237 0.070 0.264 20.122 20.155

FixedHome orwork Flexible 0-3 seconds 3-10 seconds

Over10 seconds

About 2 inches 20.270 0.215 0.153 20.028 20.133 0.142About 5 inches 20.199 0.251 0.009 0.199 0.034 20.237About 13-19 inches 0.346 20.343 20.119 20.126 0.073 0.070Small 20.199 0.274 20.017 20.247 20.005 0.264Medium 20.062 20.052 0.149 0.316 20.246 20.122Normal PC-keyboard 0.249 20.225 20.110 20.026 0.215 20.155Fixed 1 20.729 20.642 20.217 0.267 0.001Home or work 20.729 1 20.057 0.075 20.177 0.072Flexible 20.642 20.057 1 0.233 20.191 20.0820-3 seconds 20.217 0.075 0.233 1 20.463 20.6553-10 seconds 0.267 20.177 20.191 20.463 1 20.366Over 10 seconds 0.001 0.072 20.082 20.655 20.366 1

Table AI.Pearson correlations in

internet users’ data


411

Appendix 2

Corresponding authorTommi Laukkanen can be contacted at: [email protected]

About2 inches

About5 inches

About13-19 inches Small Medium

NormalPC-keyboard

About 2 inches 1 20.410 20.698 20.107 0.129 20.011About 5 inches 20.410 1 20.367 0.110 0.220 20.283About 13-19 inches 20.698 20.367 1 0.023 20.304 0.234Small 20.107 0.110 0.023 1 20.343 20.620Medium 0.129 0.220 20.304 20.343 1 20.524Normal PC-keyboard 20.011 20.283 0.234 20.620 20.524 1Fixed 20.205 20.012 0.219 20.012 20.034 0.038Home or work 0.083 20.037 20.056 0.094 20.096 20.005Flexible 0.209 0.053 20.255 20.074 0.139 20.0490-3 seconds 20.063 0.186 20.082 20.466 0.305 0.1683-10 seconds 20.153 0.106 0.073 0.360 20.209 20.152Over 10 seconds 0.201 20.279 0.014 0.132 20.111 20.028

FixedHomeor work Flexible 0-3 seconds 3-10 seconds

Over10 seconds

About 2 inches 20.205 0.083 0.209 20.063 20.153 0.201About 5 inches 20.012 20.037 0.053 0.186 0.106 20.279About 13-19 inches 0.219 20.056 20.255 20.082 0.073 0.014Small 20.012 0.094 20.074 20.466 0.360 0.132Medium 20.034 20.096 0.139 0.305 20.209 20.111Normal PC-keyboard 0.038 20.005 20.049 0.168 20.152 20.028Fixed 1 20.704 20.734 0.018 0.095 20.105Home or work 20.704 1 0.035 20.048 0.072 20.018Flexible 20.734 0.035 1 0.020 20.203 0.1650-3 seconds 0.018 20.048 0.020 1 20.446 20.5793-10 seconds 0.095 0.072 20.203 20.446 1 20.471Over 10 seconds 20.105 20.018 0.165 20.579 20.471 1

Table AII.Pearson correlations inmobile users’ data

IJRDM35,5

412

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