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Journal of Services MarketingMass customization for financial services: an empirical study of adoption and usage behaviorStefan Koch Duygu Inanc

Article information:To cite this document:Stefan Koch Duygu Inanc , (2015),"Mass customization for financial services: an empirical study of adoption and usagebehavior", Journal of Services Marketing, Vol. 29 Iss 3 pp. 235 - 243Permanent link to this document:http://dx.doi.org/10.1108/JSM-04-2014-0115

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Mass customization for financial services:an empirical study of adoption and usage

behaviorStefan Koch and Duygu Inanc

Department of Management, Bogazici University, Istanbul, Turkey

AbstractPurpose This paper aims to report findings from an exploratory empirical study focusing on an application of mass customization in financialservices. Based on the study of configurations and usage data, the authors evaluate a series of hypotheses relating to the interplay of adoption andusage by customers.Design/methodology/approach The study is based on quantitative analysis of data from a Turkish bank which offers customizable credit cards,encompassing both configurations as well as credit card usage.Findings The results confirm that trial-and-error learning will not end with product definition, but will continue afterwards and lead to changesin customization. Especially active usage length shows a significant positive effect on the number of changes. The effect of base category usage couldonly partly be confirmed for changes, but was significant for adoption. It was also found that a series of smaller changes in a limited number ofattributes has a higher likelihood than a smaller number of changes in a large number of aspects.Research limitations/implications The study uses data from a single financial service provider, from a specific country. In addition, anonymizeddata on adoption and usage were used, thus demographic data as well as subjective measures from customers were not available.Practical implications The results highlight the importance of specifying the correct solution space, as the authors could at least partially confirmthe negative effect of both a large number of options, as well as basing on alternatives rather than attributes on several levels. Although overallmass customization seems less interesting than traditional credit cards, the authors discuss several positive implications for financial sectorcompanies from offering this option.Originality/value The paper extends current literature in focusing for the first time on mass customization for financial services. In addition, thisis the first study using longitudinal data on adoption and modification of mass-customized solutions to analyze the long-term behavior of usage.

Keywords Empirical study, Financial services, Mass customization, Lead user theory, Toolkits, User innovation

Paper type Research paper

Introduction

Mass customization has been an important topic both forresearch and practice for many years now. Based on the worksof Davis (1987), Kotler (1989) and Pine (1993), it can bedefined as:

[. . .] a strategy that creates value by some form of companycustomerinteraction at the fabrication/assembly stage of the operations level to createcustomized products with production cost and monetary price similar tothose of mass-produced-products (Kaplan and Haenlein, 2006).

In this paper, we will focus on the consumer adoption andusage of a mass customization initiative in the financialservices sector, a customizable credit card. This case representan interesting case, both as an electronic mass customizationexample in Turkey, and as a customizable financial serviceproduct.

The paper extends current literature in focusing for the firsttime on such a mass customization initiative, and providing anexploratory case study. In addition, this is the first study in themass customization literature using longitudinal data onadoption and modification of mass-customized solutions toanalyze long-term behavior. The main research question forour study is which people adopt such a product in thisindustry, and which attributes affect characteristics of theirusage behavior over time. As the credit card customization canbe changed after initial adoption, we will especially focus onchanges made to the customization. The structure of thispaper is as follows: We start with a review of the existingliterature on mass customization, especially related toadoption and usage, and derive our hypotheses. The literaturereview is based on an extensive search through electronicrepositories including Google Scholar and ScienceDirect,using keywords like mass customization, adoption,acceptance and financial service with synonyms in varyingcombinations. We will then detail the setting in financialservices, methodology and data gathering. In the resultssection, we will present the quantitative analysis andhypotheses testing. The paper closes with a discussion which

The current issue and full text archive of this journal is available onEmerald Insight at: www.emeraldinsight.com/0887-6045.htm

Journal of Services Marketing29/3 (2015) 235243 Emerald Group Publishing Limited [ISSN 0887-6045][DOI 10.1108/JSM-04-2014-0115]

Received 1 April 2014Revised 8 July 2014Accepted 13 July 2014

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also gives implications, as well as limitations and futureresearch.

Theoretical background and hypothesesThe concept of mass customization was introduced in 1987for defining the mass production of personalized productswith less cost (Davis, 1987; Pine, 1993; Kaplan and Haenlein,2006). In the twenty-first century, mass customization startedto be used together with online user toolkits, and both forelectronic and factory-output products, to offer customersthe opportunity to design their own products online (Frankeet al., 2008). The primary argument for users adopting masscustomization is the delivery of superior customer value.Schreier (2006) discusses four aspects, namely, a closer fit toindividual needs, perceived uniqueness, the process ofdesigning per se allowing the customer to meet hedonic orexperiential needs and, finally, the pride-of-authorshipeffect. Resulting from those, for example, Franke and Piller(2004) have found, on average, a 100 per cent value incrementfor self-designed watches. Kaplan and Haenlein (2006)discuss to which kinds of products or services, masscustomization is applicable. They argue that due to theperishability and inseparability properties of services, masscustomization could only be used for products. Otherwise, theconcept would be misleading, as it would be about deliveringa customized service in a cost-effective way not customizing amass product. The authors propose the term modulization forservices. They also highlight that mass customization of aproduct can be considered as a service in itself. Lampel andMintzberg (1996) have grouped financial services with otherso-called menu industries under customized standardization.Nowadays, mass customization as experienced by users is

most often shaped by the Internet, and the design of usertoolkits as interfaces which enable users to plan and at leastpreview their product becomes important. Von Hippel andKatz (2002) define toolkits as a technology that allows users todesign a novel product by trial-and-error experimentation,and delivers immediate (simulated) feedback on the potentialoutcome of their design ideas. A toolkit should offer users asolution space that encompasses the designs they want tocreate, be user friendly so that there is no need for muchadditional training, contain libraries of commonly usedmodules that the user can incorporate and, finally, ensure thatresulting designs will be producible without revisions. Toolkitscan also constitute a way to shift the innovation work to leadusers (von Hippel and Katz, 2002): Adoption and usage of atoolkit can serve as a market research tool, and especiallychanges to the toolkit by lead users can provide importantinput (Prgl and Schreier, 2006).For mass customization of a financial service, in our case a

credit card, the configuration can be changed over time. Thismeans that trial-and-error learning of a customer is not limitedto (simulated) toolkit feedback to come up with a productsolution closer to his unique needs (Von Hippel and Katz,2002), but that this can be an ongoing process ofexperimentation and optimization in a real-world useenvironment. If therefore a configuration is available thatoffers an increase in utility a customer derives from theindividualized product, a customer will switch to this. Notethat we focus only on the functional benefit here, disregarding

any process benefits (Schreier, 2006) that might arise from theact of continued or repeated customization. We thereforeformulate our first hypothesis:

H1a. For mass customization of financial services, trial-and-error learning will not end with product definition, butwill continue afterwards and lead to changes incustomization, i.e. new configurations.

In addition, it can be assumed that longer periods of activeusage and thus experimentation will lead to uncovering ahigher number of improvements. We can therefore formulatethe related hypothesis:

H1b. A longer period of active use of a mass-customizedservice will lead to a larger number of changes incustomization, i.e. new configurations.

Kaplan et al. (2007) have argued for more research on thecustomer viewpoint, and have focused on the influence of acustomers base category consumption frequency and needsatisfaction on the decision to adopt a mass-customizedproduct within this category. The assumed positive effect ofbase category consumption was based on the works ofDickerson and Gentry (1983), who found that adoptersof innovations have more experience than non-adopters, aswell as Gatignon and Robertson (1985), who proposed thatnew product innovators are drawn from heavy users of otherproducts within the same category. Also, Dellaert andStremersch (2005) found that consumers with high levels ofproduct expertise consider mass customization configurationsless complex. Kaplan et al. (2007) indeed found a significantpositive direct influence on the behavioral intention to adopt,so the more frequently products out of the base category areconsumed or the more satisfied the customer is due to thisconsumption, the higher the intention to adopt amass-customized product. Extending the work of Kaplan et al.(2007) to actual adoption, as well as to continued usage, weformulate the following two hypotheses:

H2a. Customers with a higher base category consumptionwill have a higher likelihood to adopt the masscustomization option, i.e. the customized credit cardover a standard one.

H2b. Customers with a higher base category consumptionwill tend to use the mass-customized product moreintensely, i.e. use their customized card more.

The reasoning for H2b is again based on Kaplan et al. (2007),we presume that the characteristic of higher consumption isstable for a customer, so that it will also affect ongoing usage,not only the adoption. We also extend this line of reasoning tochanges in the configuration. As people with higher basecategory consumption show higher interest in new productsand innovations (Dickerson and Gentry, 1983; Gatignon andRobertson, 1985), it can be assumed that they continue theirsearch for new, innovative solutions after first configuring amass-customized product. Similarly, Dellaert and Stremersch(2005) found that consumers with high levels of productexpertise consider mass customization configurations less

Mass customization for financial services

Stefan Koch and Duygu Inanc

Journal of Services Marketing

Volume 29 Number 3 2015 235243

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complex, thus ongoing experimentation will be less costly forthem. We therefore formulate our hypothesis:

H2c. Customers with a higher base category consumptionwill tend to change their card configuration more often.

Besides the positive effects of mass customization forcustomers, the term mass confusion has been established forassociated drawbacks. Piller et al. (2005) identified threedifferent problem categories from the customers perspective:1 the burden of choice, external complexity caused by

excess variety (Franke and Piller, 2004; Huffman andKahn, 1998) with users being overwhelmed by thenumber of options;

2 matching needs with product specifications, as customersmight lack the knowledge and skills to make a fittingselection (Huffman and Kahn, 1998); and

3 an information gap regarding the behavior of themanufacturer (Franke and Piller, 2004).

Huffman and Kahn (1998) also found that attribute-baseddecisions, in which customers provide a preference rating fordifferent levels of a single attribute, lead to higher customersatisfaction than alternative-based ones. Dellaert andDabholkar (2009) showed that the range of masscustomization options and providing complementary on-lineservices enhance perceptions of product outcome, control andenjoyment, and thus intention to use. On the other hand,increasing the range of options can increase the perceivedcomplexity of the process. Dellaert and Stremersch (2005)showed that product utility increases mass customizationutility, while complexity has a negative effect. The extent towhich customers can customize a product increases utility butalso complexity.Bardakci and Whitelock (2004) also list the need for

customers to invest time in specifying their preferences as aninconvenience of mass customization. They found that a highnumber of products available in the market hinders thedecision process of consumers and thus is an advantage ofmass customization, in addition to the opportunity for priceadjustments (Bardakci and Whitelock, 2004). In a follow-up,Bardakci and Whitelock (2005) analyzed the Turkishenvironment, and found more respondents willing to pay aprice premium compared to the UK, and also keen onupdating the features of their car over time. A main reason wasthe possibility to disable unwanted aspects or functionalities tostay within budgetary constraints.In line with the presented literature, we propose that mass

confusion will be present in financial services as well. Theconfiguration toolkit offers several possible ways of coming upwith a customized credit card. Based on the work of Huffmanand Kahn (1998), those avenues that are based on attributesshould lead to less mass confusion and therefore a higheradoption. Similarly, the number of options, or extent of scope(Schreier, 2006), is assumed to have a negative effect due tothe resulting mental burden (Franke and Piller, 2004; Dellaertand Stremersch, 2005; Dellaert and Dabholkar, 2009;Huffman and Kahn, 1998). In addition, we propose that theeffect of burden of choice as an aspect of bounded rationality(March, 1978) will also affect changes to configurations. Itshould therefore be less of a cognitive load to make changes to

a small number of card attributes at a single time as comparedto changing several. Also, psychologists have demonstratedthat small changes are preferred to large changes (Weick,1984), and also most innovative processes are incremental andbuild on accumulation. We therefore formulate the followinghypotheses:

H3a. Mass customization options based on attributes will bemore likely to be adopted than those on alternatives.

H3b. A larger number of options will decrease theattractiveness of a mass customization option.

H3c. A series of changes to a small number of attributes of aconfiguration will place less cognitive load oncustomers than a smaller number of changes in a largenumber of aspects, and therefore will have a higherlikelihood to be used.

Setting and methodologyMass customization has as yet not been adopted widely infinancial sector products, although financial servicesconstitute an important service category (Oliveira and vonHippel, 2011), and the roles of both user innovation (Oliveiraand von Hippel, 2011) and user involvement (Alam, 2002), aswell as customerization (Wind, 2001), have been discussed inthis context. There is very limited research on this setting formass customization according to our literature search.Papathanassious (2004) has found high interest in masscustomization in the financial sector in a survey, especially asthis sector is characterized by dynamically changing customerneeds, high heterogeneity and quite fast technologicalchanges, plus is information-intensive. Winter (2001) hasdescribed both a product-oriented and a process-orientedapproach to individualization of financial services. Wright(2002) highlights that new distribution and processingtechnologies on the supply side and changes in consumerattitudes to banking on the demand side have driven the globalbranding of retail financial services, because strong brandshave the established customer and distributor franchiseneeded, and because global brands are a means for consumersto identify reassuring and trustworthy products and services inthis context.The bank considered in our study was pioneering the first

credit card in the world that offers customization to its users.Interest rate, annual card fee, installment properties,rewarding mechanisms and visual aspects of the card can becustomized by the customers. For this task, a toolkit isavailable to Web users who are applying online. This toolkitsupports all customizable properties of the product and usershave a chance to play with the parameters, and the effects oftheir changes on the product are reported and visualized ifapplicable.There are several ways for the users to create their

customized credit card. In the first alternative, users areoffered to customize main variables, including the visual of thecard, reward/installment rate, interest rate, annual paymentand sectors/firms of interest. They can also choose a campaignfor which they can then customize several variables. Thesecampaigns are Select Sector to change the sector in whichthey can gain extra rewards or installments, Select Firms to





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select three firms for this, Highest Expense to get extrainstallment/reward in the highest expenses, MomentaryCampaign and No Campaign for full customization. As asecond alternative, there are several optimized packages forthe main card variables available: Extra bonus/installmentpackage (Extra Bonus), both reward and installmentpackages and minimum payments package according tofinancial parameters. On the other hand, there are sectorpackages such as communication (Communication),transportation (Travel), etc. which provide advantages inthose sectors. Finally, a recommendation system is availablewhich tries to identify the users preferences by askingquestions such as do you usually pay the all of your card debt,or some of it?. The multiple-choice answers are then mappedto the ready-to-use packages and the most appropriatepackage is proposed. The credit card is then initialized withthe chosen parameters and delivered to the customer. Thecustomer is able to change the parameters using Internetbanking afterwards.The research presented here can be characterized as an

exploratory case study, the aim was to analyze the adoptionand usage of the product by looking at the life cycle of creditcards. Therefore, a study of the toolkit interface was notenough, detailed card history including usage data wasnecessary. We used, as Franke and Piller (2003) recommend,observed data instead of self-reported measures. Therefore,contact was established with the bank, especially themarketing department and the technology subsidiarymaintaining the IT systems. The confidentiality of customerinformation was a major discussion point, and subsequentlyall data have been strictly anonymized for all analyses.At the start, several interview sessions with different

employees familiar with the credit card were held about thecard itself and the application process. The main goal was todetermine the available and relevant data. The technologysubsidiary and marketing department approved access to thecustomization properties and customer choices during andafter the application process, as well as to the data on creditcard usage, after confidentiality of customers and theunderlying card pricing model, which is considered relevantfor competition, was assured. After the data have beenretrieved from the production system through a databasequery, they were imported into a new separate database set upfor performing queries and analyses on the data. The datawere checked both by the bank as well as the researchers forduplicates and extreme or impossible values. The analyseswere done using both database queries for descriptive statisticsand a statistical package.To be able to analyze the amount of data retrieved, some

form of sampling out of all existing cards was necessary. Forthis study, all (approved) applications for a customized creditcard entered during the second half of 2008 were selected, andtheir progress and usage was tracked until the end of 2009.During the selected period of six months, there were 11,590approved applications for a customized credit card. Theseapplications and the resulting credit cards with their usagehistory therefore form the complete data set for the empiricalstudy. One factor that contributed to this decision was thatthere had been major changes in card packages and structurein 2007, which would have made any comparisons

problematic. A period of at least one year was deemed longenough to analyze a customers adoption and usage behavior.In addition, more recent data were not made available due toprivacy and competition concerns. In the following analyses,the initial credit card configuration, all changes to theconfiguration, the configuration at the end of 2009, statementsummary amounts for all monthly statements during 2009 andoverall yearly transaction volumes and amounts of all creditcard types offered by the bank are used.

Analysis and results

Adoption and initial configurationsWe first retrieved the total volumes for normal and customizedcredit cards. In 2009, total volume was 3,552,550,626Turkish Lira (TL), about 2,368,367,000 USD at 2009exchange rate, from 23,400,213 transactions, comparedto 29,558,237,666 TL (19,705,492,000 USD) from433,417,897 transactions for the traditional credit card line ofthe same bank. In total volume, the customized credit cardtherefore is much smaller than the traditional offering. On theother hand, mean transaction volume is significantly higher at151 TL compared to 68 TL (p 0.01). This confirms ourH2a, customers with a higher base category consumption havea higher likelihood to adopt the mass customization option.Next, we focus on the initial selections of customers when

adopting the mass customization option to evaluate H3a andH3b. Two different package types can be distinguished,predefined or customized. In the first case, no furthercustomization beyond selecting the package takes place; forthe other packages, the user has several options. From theseinitial choices, it is interesting to see that althoughcustomizable packages are chosen slightly more often than thepredefined packages (with 51.3 per cent vs 48.7 per cent), thedifference is not significant (chi-squared test statistic, p 0.01). With regard to pricing and conditions, none isnoticeably more advantageous than the other. Possible reasonsfor this distribution are a user interface that could makefurther customization too cumbersome, or a well-thoughtdesign of predefined packages that capture a huge part ofpossible segments and customization elements of interest. Onthe other hand, both motivational aspects like enjoyment andunserved heterogeneity make customized packages at leastequally successful. The full customization package withoutany campaign selection is actually the most often chosenpackage and at the same time represents the highest level ofcustomization. This provides a first support for H3a, as thecustomization in that case is based on attributes and not aselection of alternatives.If the choice of packages is more closely analyzed, as

explained No Campaign is most often chosen (17.36 percent). In this offer, the users are allowed to determine the extrabonus rate, late payment interest rate and the card fee resultsfrom these choices. Card fees range between 16 TL and 117TL depending on the interest rate and extra bonus ratios. Forthe predefined packages, the maximum card fee is 60 TL.Users can select up to 1 per cent extra bonus in everytransaction they pay for, but predefined packages are limitedat this point. Users can also decrease interest rate down to2.68 per cent. No such predefined package combination isoffered. The fact that this customization is chosen most often





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hints at the fact that the customization flexibility is importantto this group of customers. This provides additional supportfor H3a.Furthermore, packages focused on additional rewards or

installments follow in frequency with Extra Bonus (16.56per cent), Select Sector (14.07 per cent) and HighestExpense (12.49 per cent). Sector-targeted predefinedpackages on the other hand are very undesirable in contrast toflexible sector selection, clothing being most successful with2.29 per cent. Sector selection also enables customers to selectsectors other than the predefined ones, and from time to time,multiple sector selections can be made. The popular packagesshow that offering extra bonus or installment is the mostpreferred property in customization.With regard to the least often chosen campaigns, two of

them are predefined packages with targeted segments only,Travel (0.28 per cent) and Communications (0.40 percent), in addition to Firm Selection (0.79 per cent). Thereare several different products on the market with morespecified and advantageous travel options than thesecampaigns, one also from the same bank. So these campaignsmay be affected by cannibalization effects. On the other hand,the Firm Selection package seems to be the most complexcustomization offered, in which users search for firms in theinterface, select three specific firms and finally determinewhether they want to gain extra bonus or extra installmentfrom these, as well as the possible minimum limits for such again. In that case, an overwhelming amount of possibilities,coupled with the maximum limit of three choices, seems tohave negative effects. This provides support both to H3a andH3b, as this option is based on alternatives, and provides avery large number of options as well. We can also deriveadditional support for both hypotheses from the generally lowuptake of the mass customization option, as within the masscustomization, the first step is alternative-based packageselection with a relatively large number of options.

Longitudinal analysisWe will now turn to the analysis of the events and usage of acustomized card after creation. The first aspect is the amountof changes made to existing cards during the observed period.Overall 4,675 cards of the 11,590 total showed a difference inat least one major aspect between approval and the end of ourtime window. Any in-between changes are not accounted forin this analysis, e.g. if a card had reverted to the prior package,no change would be recorded. Although not all cards havebeen changed, we certainly find that customers do actuallyengage in trial-and-error learning after initial configuration.This confirms H1a.We also analyze which attributes of a card have been more

prone to change (Figure 1), and the amount of changes(Figure 2). When a comparison between the application dataand the present attributes of a card is made, it is found that3,202 cards among 11,590 have a different plus reward/bonusrate. This can also be interpreted as an aspect of H3a, aschanges in an attribute are much more common than in analternative, i.e. change in package. We can also analyze thenumber of attributes changed for a card, and we find that mostcards have a change in one aspect only (Figure 2). Thisprovides first (partial) support forH3c, as complete changes of

a customization, i.e. changing several attributes, areuncommon.For more detailed analyses related to usage characteristics,

further sampling was necessary due to the amount of data, asa cards life cycle for over a year may include many changes inthe properties and a number of statements. A sample of 500cards has been chosen by simple random sampling method,we ascertained that it has the same distribution with regard tochosen packages as the full population.First, the number of changes made is related to the total

amount spent during the time of observation as a measure ofbase category consumption (according to H2c). The results(Figure 3) show that most cards experience several changes,with the majority between 4 and nearly 30. A change means anew configuration of a card, and thus can include any numberof changes in properties on all levels. In this analysis, we alsoaccount for all intermediate steps plus small adjustments, thusthe number of changes is much higher than in the previousresults on a global level (Figure 2). This clearly provides

Figure 1 Number of cards showing a change in a certain propertybetween creation and end of observed period

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Figure 2 Number of changes in a property between creation andend of observed period for a card

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support for H1a relating to the existence of continuedtrial-and-error learning during use, as well as H3c related tocustomers preferring a series of small scale changes.Confirming H2c, there is a clear and significant positivecorrelation between the number of changes and base categoryconsumption as expected, at 0.406 (p 0.01). For allcorrelations, the non-parametric Spearman coefficient is used,as variables are not normal distributed.While the total amount spent by a card gives some

indication of behavior during the observed period, someeffects like different creation dates or non-homogeneousdistributions over time (e.g. including a wedding or vacations)might introduce some bias. We therefore retrieved the numberof statements issued (which is the number of months the cardwas actively used) for the evaluation of H1b. This shows aclear relationship to the total number of changes according toand confirmingH1b, with a correlation coefficient of 0.76 (p0.01), highlighting that customers who use the card activelyfor a longer period change the customization more often. Theaverage number changes per active month is negativelycorrelated with the number of statements though (correlationcoefficient of 0.7, p 0.01), which could hint at an optimal orat least sufficient solution being reached after some time. Withregard to the impact of usage on base category consumption,i.e. total amount spent, again a positive correlation of 0.57(p 0.01) shows up for the activity time, confirming H2b. Wealso compute the average spending per month for a card, and

again relate to the number of changes. The correlationcoefficient becomes distinctly smaller but remains significantat 0.16 (p 0.01), demonstrating a positive relationshipbetween number of changes and monthly spending, providingadditional support to H2c. The number of statements is alsopositively correlated to the average spending at 0.26 (p 0.01), again confirming H2b.We also analyze the card fee as a construct for available

resources of a customer. Card fees are determined by theuser through package properties, with more expensivecombinations more beneficial in some respect, mostlyusage-dependent. We therefore check for any relationshipbetween the total card fees paid and the total spending, as wellas between average values. For the summary values, acorrelation coefficient of 0.48 (p 0.01) can be found, foraverage values the correlation is not significant. The numberof statements naturally is highly correlated to total card fee (at0.77, p 0.01), but not significantly to the average card fee.To aggregate these findings, we perform a multiple

regression analysis, first with the transaction amount asdependent variable, as this is the aspect of highest interest formarketing applications. We estimate both a model for the totalas well as average transaction amounts. As predictors, we arelimited to the variables retrieved, so demographic controls cannot be added. We therefore include the initial packagenumber, card fee (either total or average depending ondependent variable) and the number of changes. We estimatethe full model and then also use stepwise multiple linearregression analysis, using Akaike information criterion(Table I). In both cases, but especially for the average amountspent, the quality of the models is relatively weak. For the totalamount spent, the number of changes to the customization toa card and the total card fee provide some explanatory power,interestingly it is only the initial package selection for averageamount spent. As an underlying explanation, both number ofchanges and total card fee are time-sensitive with regard tousage. This seems to indicate that differences in total spendingare more related to continuous, but not necessarily highspending per month. Customers who use their customizedcard frequently over time accrue high total transactionvolume, and continuously adapt the card to their wishes.Contrary to that, people with more infrequent but high usage,resulting in high average amount per month, do not changethe customization often. An example could be acard-optimized and used exclusively for a wedding, resultingin high expenditure but no changes over a short period. If thenumber of months active is added as an explanatory variable,it is the only one that remains in stepwise regression.

Figure 3 Total amount spent by a card plotted against number ofchanges to customization (N 500)

0 10 20 30 40 50 60 70 80 90 10050,000.00

0.00

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Table I OLS regression results for transaction volumes

VariableDependent variable: total amount spent coefficient (p-value)

Full model Step 1 Step 2

Number of changes to customization 165.854 (0.18) 189.708 (0.12)Initial package selection 270.803 (0.21) Total card fee 15.821 (0.00) 19.007 (0.00) 16.196 (0.00)Average card fee R2 (adjusted R2) 0.08 (0.08) 0.07 (0.07) 0.08 (0.07)F-value (p-value) 9.92 (0.00) 25.63 (0.00) 14.08 (0.00)





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A second regression analysis is performed to assess influences onchange behavior. This allows us to provide further evaluation ofH2b relating changes to base category consumption, and H1brelating it to usage length. In that case, we add all availablevariables, including the activity period (Table II). As can be seen,only the number of statements as a proxy for length of usage, plusthe initial package selection significantly influence the amount ofchanges made. The transaction volumes and card fees are notsignificant as predictors. This analysis therefore confirms H1b,but does not support H2b.

Discussion and conclusion

Summary and analysisIn this paper we have analyzed an example of masscustomization in the financial services sector. Our exploratorycase study has focused on adoption and usage of acustomizable credit card offered by a Turkish bank, as well asfor the first time, a longitudinal analysis of usage and changes.Overall, the attractiveness of the customizable offering seemsto be small in number of customers as well as in total volumescompared to traditional credit card services offered by thesame bank. The customizable credit card accounts for about10 per cent of the volume of the most successful product fromthe same bank, although average transaction volumes arelarger. Our H2a, that customers with a higher base categoryconsumption have a higher likelihood to adopt the masscustomization option, was confirmed. This confirms andextends the findings of Kaplan et al. (2007) for intention toactual adoption and usage. Also, lead user theory (von Hippel,1986) can offer an explanation, as lead users generally, amongother factors, show a higher use (Schreier and Prgl, 2008) orproduct experience (Dellaert and Stremersch, 2005). Somereasons for this low adoption rate could also be the marketingpolicy of the bank or the understanding of customers aboutthe product, basically relating back to a possible massconfusion problem (Huffman and Kahn, 1998; Franke andPiller, 2004; Piller et al., 2005). When analyzing customerchoices, we found at least partial support for H3a, that masscustomization options based on attributes will be more likelyto be adopted than those on alternatives, and H3b, relating tothe negative effect of a larger number of options on theattractiveness. We found that one of the least preferredpackage is selecting firms which have the most complexinterface, resulting in several levels of process complexity

(Dellaert and Dabholkar, 2009) and considerable burden ofchoice based on alternatives (Franke and Piller, 2004;Huffman and Kahn, 1998). Also, pre-existing concepts aremost likely to be used, which therefore allow for easier needmatching (Huffman and Kahn, 1998). On the other hand,the most often chosen package allowed for fullcustomization through attribute-based customization, whilepre-defined packages, especially limited ones, were lesssuccessful. We can conceptualize the different packages asdifferent toolkits, with those that are already over-defined asbeing too narrow in their solution space (von Hippel andKatz, 2002). The fully customizable option also allows toturn off unwanted features and come to the lowest possibleprice, a feature found by Bardakci and Whitelock (2005) tobe especially important in the Turkish environment due tobudgetary constraints.The longitudinal analysis of usage including change

behavior is a mostly under-researched topic in masscustomization literature. We found that most aspects of a cardare not changed, but that the number of changes in changedaspects is relatively high, showing fine-tuning behavior,respectively, learning by doing via trial-and-error (von Hippeland Katz, 2002). Our related H1a was therefore confirmed.The pattern of changes provided at least partial support to ourH3c that a series of smaller changes is used more often. Wefind that cards with a higher number of changes are also thosewhich show a higher total turnover and, to a smaller extent,average monthly turnover. Our H2c, extending current theoryon the positive effect of base category consumption to a higherrate of changes, was only partially confirmed throughcorrelation analysis, but was not significant in the regressionanalysis. The most important influence factor on thenumber of changes was found to be the active usageduration. Customers who use their customized cardfrequently and consistently over time accrue high totaltransaction volume, and they continuously andmeticulously adapt the card to their wishes. This secondaspect confirmed our H1b, that longer period of active usewill lead to a higher number of changes in configuration,and also resonates the findings of Bardakci and Whitelock(2005) for the Turkish car environment. Similarly, Prgland Schreier (2006) found that users of toolkits are notone-time shoppers, but that their innovative engagementis rather long-lasting, continuous, evolving and intense.

Table II OLS regression results for change behavior

Variable

Dependent variable: number of changes to customization coefficient(p-value)

Full model Step 1 Step 2

Initial package selection 0.223 (0.01) 0.223 (0.01)Total number of months active (Number of statements) 1.115 (0.00) 1.442 (0.00) 1.394 (0.00)Total amount spent 0.000 (0.98) Average amount spent 0.003 (0.73) Total card fee 0.006 (0.27) Average card fee 0.032 (0.48) R2 (adjusted R2) 0.33 (0.32) 0.31 (0.31) 0.32 (0.32)F-value (p-value) 26.01 (0.00) 145.4 (0.00) 77.16 (0.00)





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ImplicationsFinancial products are not the outcomes of a manufacturingprocess as in traditional mass customization, but constitute, inmany cases, long-term services. Differentiation throughcreating alternatives can constitute a competitive advantage tobanks. There were many credit cards with different targetingoffered to the customer, but a mass-customized credit cardconstituted a new complementary offering. Kotha (1995) hasdescribed a company active both in mass production and masscustomization. This is similar to the case here, and can providea competitive advantage. Kotha (1995) described thatcompetitors have been forced to offer mass customization aswell, probably leading to a change in industry conditions,which is not yet visible for our case. The author also noted thatcompeting in two segments might lead to problems stemmingfrom different priorities, mostly relating this to productionaspects (Kotha, 1995). This is not a factor in our case, butmarketing aspects are, as both initiatives basically compete forthe same budgets.A second aspect of offering a toolkit to customize a financial

service beyond as a marketing strategy can be to learn aboutcustomer preferences. Our analysis has shown very differentadoption rates for different packages, as well as the ability togather data about changing behavior and relationships to cardusage. This can provide important feedback to productdevelopment. Prgl and Schreier (2006) also found thatsometimes leading-edge users do not merely contentthemselves with the official toolkits, and either develop theirown or provide ideas. In addition, individual user designs arenot only attractive to the creators themselves but can also be inhigh demand among other users. This means that providing atoolkit for customization can be a way of getting ideas for newproduct development, or even to identify lead users (vonHippel and Katz, 2002; Prgl and Schreier, 2006). Also Alam(2002) discusses user involvement in new servicedevelopment, using cases from the financial servicesbusiness-to-business sector. He reports that user involvementcan help companies to achieve more superior anddifferentiated services. Oliveira and von Hippel (2011) havealso focused on banking services, and found that in themajority of cases, users self-provided a novel service beforeany bank offered it.The results of this study also confirm the difficulties of

designing an appropriate toolkit and solution space, aspackages that specify a card too narrowly already are nothighly adopted. On the other hand, also a too wide solutionspace, e.g. encompassing every single possible shop forselecting benefits, proves problematic. We also found evidencefor extremely targeted customization, cards created forproviding maximum benefits for limited applications like awedding. Those incur high transaction volumes in a shorttime, and are seldomly changed. It should be noted that thepricing for customized cards is problematic for the bank in ourcase study. As a solution to clearly existing mass confusionproblems, an online community to allow customers to discussthe customization process, share creations and support eachother (Franke et al., 2008; Piller et al., 2005) would be aninteresting solution and increase the popularity of the card.The bank in our study is reluctant to do so, as the pricing andtheir experience shows that there are some sweet spots in

the configuration, and therefore it is not in their best interestto enable this kind of sharing and communication.

Limitations and future researchThis research has several limitations which also provide areasfor future work, and should be seen as a starting point forfurther research on this topic. The first area of possibleconcern is external validity. The study is based on a single caseof mass customization in the sector of financial services. Thegeneralizability of the results regarding longitudinal aspects toother industries is unclear. Similarly, the Turkish environmenthas found to be more ready to adopt mass customization thanthe UK market (Bardakci and Whitelock, 2005), and theresults might be influenced by this as well as other culturalfactors. Naturally, in further research, different industries aswell as different cultural settings would be interesting toexplore and compare, in addition to gathering data fromseveral institutions. The bank used is a leader in the field in thecountry, as well as one of the largest banks with over 10million customers, which could hint at a relativelyrepresentative customer structure.A second concern is construct validity. The study presented

here has relied on observed data gathered from the databasesof the participating bank. Any kind of error in that data wouldalso transfer to our study. The data were checked by the bankand the researchers for duplicates as well as any extreme orimpossible values. Although any other error can nearly beeliminated as a concern for some aspects like credit cardstatements, the number of changes to a customization is lessclearly defined in literature. Further work should considerusing measures of distance or similarity to configurations. Inaddition, no personal data were available, so two cards in oursample could belong to persons in the same family (but not tothe same individuals, as bank guidelines do not allow for that),or a person could own both a customized and a traditionalcard. This could have an effect on the results, although therelatively high mean transaction volumes might be anindication that this is a smaller problem. Although privacynaturally has to be a major consideration, the use ofanonymized identifications would be a remedy for this infuture studies. In such a case, the data could probably also beaugmented with demographic aspects for research onadoption and usage.Finally, customer-customer environments as a complement

to mass customization (Piller et al., 2005; Franke et al., 2008)are an interesting area. Unfortunately, the participating bankhas not agreed to implement such an environment, butresearch to study the effects of such an environment onconfiguration change frequency or even configurationconvergence would be highly relevant.

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Corresponding authorStefan Koch can be contacted at: [email protected]

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Mass customization for financial services: an empirical study of adoption and usage behaviorIntroductionTheoretical background and hypothesesSetting and methodologyAnalysis and resultsAdoption and initial configurationsLongitudinal analysis

Discussion and conclusionSummary and analysisImplicationsLimitations and future research

References

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