Pechpeyrou 2009 - How_consumers Value Online Personalization

How consumers value onlinepersonalization: a longitudinal

experimentPauline de Pechpeyrou

Institut du Management et de la Distribution, University of Lille II,Roubaix, France

Abstract

Purpose – The ability to acquire and process consumer information online has provided web-basedvendors with the ability to personalize their merchandising at a very low cost. However, empiricallyestablishing the expected positive effect of personalized merchandising has been difficult for practicalas well as financial reasons. The aim of this paper is to compare the effectiveness of personalized vsrandom merchandising on consumers’ attitudes and behaviors.

Design/methodology/approach – A longitudinal subject experiment comparing standardized vspersonalized merchandising was adopted. A fictitious web site was created for the purposes of thestudy.

Findings – Personalized items led to more clicks than random suggestions. Moreover, a positiveattitude towards personalization enhanced the attitude towards the web site.

Research limitations/implications – Even if credibility was enhanced thanks to the web sitedesign, the research suffered from a lack of external validity. Additionally, the procedure prevented usfrom observing any potential effect on basket size.

Practical implications – A strategy of personalizing the content appeared to be relevant for website managers. They should use “close” recommendations rather than “broad” recommendations andpresent a moderate number of personalized suggestions.

Originality/value – The research is one of the few online experiments with a longitudinalperspective, which is considered necessary when studying consumers’ reactions to the personalization“process”.

Keywords Consumer behaviour, Internet, Customer loyalty

Paper type Research paper

IntroductionThe internet era has deeply modified the seller-customer relationship. Indeed, eachvisitor may be personally identified through his IP address and his navigationalbehaviour may be tracked from visit to visit. His preferences may then be inferred fromhis navigation and buying behaviour (Resnick and Varian, 1997). Thanks to filteringalgorithms, web sites are able to automatically adjust “information content, structureand presentation tailored to an individual user. Personalization chooses content for theuser automatically, without direct user requests; the process of choosing contentremains hidden” (Perugini and Ramakrishnan, 2003). Three factors enable commercialweb sites to adopt such a strategy at a decreasing cost:

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

www.emeraldinsight.com/1750-5933.htm

The author would like to thank the two reviewers for their comments and suggestions whichhelped to improve the quality of the paper. The author also would like to thank Professor PatrickNicholson for his encouragements.

How consumersvalue online

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Direct Marketing: An InternationalJournal

Vol. 3 No. 1, 2009pp. 35-51

q Emerald Group Publishing Limited1750-5933

DOI 10.1108/17505930910945723

(1) the extremely large amount of user behaviour data tracked in online sites;

(2) the availability of powerful personalization techniques in commercial CRMsystems; and

(3) the easiness of implementing interaction strategies on the web (Yang andPadmanabhan, 2005).

Many studies in the information systems field have dealt with onlinepersonalization. Ho (2006) proposed organizing these numerous studies in threegroups: applications of the personalization technology, privacy concerns anddata-mining technologies (Perkowitz and Etzioni, 2000). Surprisingly, onlinepersonalization has not been studied very widely in the marketing field.In particular, the relationship between personalization and consumer response hasbeen quite neglected:

Works on this area concentrate on computational procedures to sort out transactions andpersonal profiles. Although these studies look into various aspects of personalizationapplications, little attention has been paid to the theoretical basis for understanding therelationship between personalization and user behaviour (Ho and Tam, 2005, p. 96).

Yet, such an understanding would be beneficial to web site managers who think theycould increase their benefits through cross- and up-selling.

From a theoretical point of view, personalizing the content proposed to eachindividual customer represents the most advanced step in online relationshipmarketing (Schubert and Ginsburg, 1999). It could solve the online assortment problem(Salerno, 2001; Schubert and Ginsburg, 1999) and therefore increase the web siteappreciation by working on the content relevance for individuals (Ladwein, 2001).Eventually, it should increase customer loyalty (Salerno, 2001). There is indeed someagreement that online personalization may lead to increased repeated visits and higheramounts spent per visit:

Revisits can be encouraged in a number of ways, for example, by offering valuableinformation on the web site, by changing some of the content frequently so that something isalways new, by offering personalised services, and by providing unique events, such ascontests (Supphellen and Nysveen, 2001, p. 341).

However, web site personalization may also decrease value if recommendations areperceived as irrelevant or coming from an incompetent system (Wallet-Wodka, 2003).In addition, some visitors may be strongly opposed to the use of their personal data forcommercial purposes (Treiblmaier et al., 2004). Surprisingly, to the author’sknowledge, few studies have demonstrated these effects.

Our research objective is therefore to fill up that gap by:. providing a conceptual framework to understand consumers’ beliefs and

attitudes towards online personalization; and. testing how consumers value different forms of online personalization (type and

number of items recommended).

The paper is organized as follows: first, we propose a conceptual framework tounderstand the impact of online personalization on customers’ beliefs and attitudes. Next,we present our methodological approach to test online personalization effectiveness.

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Finally, results and the main contributions are discussed, and we offer some directions forfuture research.

Conceptual frameworkAttitude towards the web site reflects the entire web site experience. It has beendefined as “a predisposition to respond negatively or positively to a web site in aparticular exposure situation” (Chen and Wells, 1999, p. 28). It is necessary to use abroad measure of attitude towards the web site in order to accurately predict intentionsto purchase a specific product and the willingness to visit the web site again (Karsonand Fisher, 2005).

Over the recent years, several researchers have proposed scales for measuring thenavigation experience and efficiency or interactivity of a web site (Loiacono et al., 2002;Wolfinbarger and Gilly, 2003; Zeithaml et al., 2002; Eighmey, 1997; Chen and Wells,1999; Chen et al., 2002; Ranganathan and Ganapathy, 2002; Aladwani and Palvia, 2002;Wu, 1999; Ghose and Dou, 1998). The number and the nature of these dimensions arestill hotly debated (Zeithaml et al., 2002; Muller and Chandon, 2004). Chen and Wells(1999) demonstrated that three dimensions explained most of the variance of attitudetowards a web site, namely information, entertainment and organization. These threefactors have a positive influence on attitude towards the web site (Muller and Chandon,2004) and explain its performance level (Dandouau, 2001). Therefore, we hypothesize:

H1. The three dimensions of a web site, namely: (a) information, (b) entertainmentand (c) organization have a positive impact on the attitude towards the website.

We build on services marketing literature (Mittal and Lassar, 1996; Surprenant andSolomon, 1987) to suggest that online personalization could have a positive impact onattitude towards the web site.

Personalization has a significant impact on five criteria: quality of work, quality ofservice, overall satisfaction, willingness to recommend and propensity to switch(Mittal and Lassar, 1996). Option personalization only affected trust in the bank andsatisfaction with the offer (Surprenant and Solomon, 1987). As the available optionsincreased, both trust in the bank and satisfaction with the offering also increased.Programmed personalization exerted strong effects on all three types of evaluationmeasures – evaluations of the employee, the bank, and satisfaction (Surprenant andSolomon, 1987). Similarly, we hypothesize:

H2. Attitude towards personalization has a positive impact on attitude towardsthe web site.

H3. Attitude towards personalization is an additional antecedent of attitudetowards the web site which increased the variance explained.

Qualitative research on 13 web users was performed to understand consumers’reactions and feelings to online personalization. The content analysis of thesemi-structured interviews confirmed our review of the literature and led to a typologyof consumers’ benefits and liabilities associated with personalized web sites.

Personalized online merchandising offered three types of benefits to the visitor:cognitive, financial and experiential.


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Cognitive benefit from web site personalization resulted from reduced search andcomparison costs to find the best alternative (Schubert and Ginsburg, 1999). However,few empirical papers have examined the role of personalized communication inreducing information overload and making customer decisions easier (Arora et al.,2008).

Stone and Gronhaug (1993) established that the predominant risk dimensions werefinancial and psychological. When shopping online, the consumer mainly faces afinancial risk if the product he chooses is not the best according to his preferences.Financial benefit referred to the reduction of perceived risk associated with a wrongchoice decision. Swaminathan (2003) assumes that a recommendation agent isparticularly useful when perceived risk is high.

Experiential benefit referred to the discovery of unknown and surprising productsthat the visitor would like, increasing the value of the experience (Hirschman andHolbrook, 1982).

Three types of liabilities were also identified through the qualitative study:commercial annoyance, low-perceived quality and excessive quantity. Commercialannoyance referred to the visitor feeling that his freedom to browse the web site isrestricted and hampered by intrusive commercial messages (cf. Bitner et al., 1990, inthe service marketing literature). Low-perceived quality arose when consumers heldthe belief that the web site would never guess the true decisional logic and the truepreferences of the visitor. An excessive quantity of suggestions referred to theperceived excessive pressure from too many messages sent.

Adopting a benefits and sacrifices approach leads to postulate (Figure 1):

Figure 1.Conceptual model

H4b

H4c

H4d

H4e

H4f

H3

H2

H1c

CognitiveBenefit

FinancialBenefit

ExperientialBenefit

CommercialLiability

QualityLiability

QuantityLiability

Attitudetowards

Personalization

Attitudetowards

the Website

Information Entertainment OrganizationH4a

H1a H1b

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H4. Attitude towards personalization is positively influenced by perceivedcognitive benefit (a), perceived financial benefit (b), perceived experientialbenefit (c) and negatively influenced by perceived commercial annoyance (d),perceived low quality (e) and perceived excessive quantity (f).

MethodologyEmpirically establishing the expected positive effect of a personalized merchandisingis difficult for three main reasons. First, commercial web sites have no interest insuggesting “placebo” recommendations which might undermine their reputation.Second, visitors’ trust –in particular the credibility and integrity facets– in the website may be reduced if they realize that the so-called “personalized” recommendationsare not personalized at all. Third, longitudinal experiments are quite rare in marketingstudies. Indeed, as underlined by Lawrence et al. (2001), it is extremely difficult toestablish a personalization effect in a “natural” context:

[. . .] in order to quantify the impact of the recommender system, it would have been useful tohave a control group of customers who received “placebo” recommendations, such as a list ofrandomly chosen products. This approach was not feasible since we were dealing with a livesystem with real customers doing real shopping.

For these reasons, personalization systems have been evaluated mainly through“fictitious systems” (Table I). One noticeable exception is the experiment reported byLawrence et al. (2001). Participants were real Safeway customers, in their usualshopping environment. Each customer participating in the program was issued with aPDA which ran a consumer application enabling the user to build a shopping list andsend it to the server. Products in the order could be chosen from three personaldatabases stored on the PDAs: personal catalogue, recommendations, and specialoffers.

Our research issue, understanding consumers’ reactions to online personalization,implied that the web site was able to capture consumers’ preferences and to adapt thepresented items on the following visits (Ho, 2006; Miceli et al., 2007). Therefore, weneeded at least two series of consumers’ visits to test our framework. Such alongitudinal approach is very rare in marketing studies since they mostly adoptcross-sectional designs. One exception is the doctoral dissertation of Stoecklin-Serino(2005) who concluded as to opposite effect: personalization techniques had a negativeimpact on trust building processes.

System implemented in areal situation

System not implemented in a realsystem

Quality of the system computedautomatically from data

Lawrence et al. (2001) Mobasher et al. (2000, 2002)

Quality computed based on inputfrom human subjects

Geyer-Schulz and Hahsler (2002),Herlocker et al. (2000), Lin et al.(2002), Sarwar et al. (2000) andYu et al. (2001)

Source: Adapted from Yang and Padmanabhan (2005)

Table I.A grouping of some of theapproaches to evaluating

personalization systems


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A commercial web site with a fictitious brand name was built by a professional agency,offering approximately 6,000 cultural products (books, CDs and DVDs). The web sitecould be visited at the address: www.abcculture.fr. Each item had a picture and wasdescribed by several attributes, such as title, author, artist, language, year, category,and subcategory. The web site structure looked the same for all visitors. The fictitiousnature of the web site was indicated at the web site entrance by the following text:

This web site is a fictitious one; it has been created for research purposes. You can browse theweb site as you would do with a real one, put items in your shopping cart, but you cannot buyitems. We wish you a pleasant visit.

Respondents were recruited from a French online panel. They received an e-mailasking them to visit the web site twice. When opening the link, the following procedureappeared:

Welcome to www.abcculture.fr: you can browse as you like to discover this web site and thenput your five preferred items in the shopping cart! A lottery will take place at the end of theresearch and some shopping carts will be offered free to their owners.

The procedure was the same on the second visit. However, between these two visits,the front page had been adapted and contained personalized product recommendationsbased upon the attributes of the products which had been selected.

The research design crossed two factors: type and number of items presented.The first factor in the research design was the type of personalized items. Many

works in the information systems field have compared algorithms in order to improveaccuracy and utility of recommendations (Ansari et al., 2000). There are two mainapproaches: proposing items appreciated by similar consumers (collaborative filtering)or proposing items according to the preferred items’ attribute values (informationfiltering). We chose the second approach for managerial and practical reasons. Wecompared two algorithms: items sharing most attribute values of the purchased items(“close” recommendation condition) and items belonging to the most deeply visitedgeneral category (“broad” recommendation condition). The operationalization of thisvariable was made in the following manner. In the baseline condition (nopersonalization), items were randomly selected in the whole database, apart fromthe five items already presented during the first visit (to check for a “novelty” effect). Inthe “close recommendation” condition, the algorithm was based on the characteristicsof the five items chosen at the end of the first visit. For example, if the item was amovie, another movie with the same main actor or, if no such item existed in ourdatabase, by the same director, was proposed. In the “broad recommendation”condition, the algorithm was based on the navigational data. Frequencies werecomputed and items belonging to the most frequently visited category (for instance,poetry books) were recommended.

The second factor in the research design was the number of items presented. Indeed,web sites greatly vary in the number of personalized recommendations they propose tovisitors (Murthi and Sarkar, 2003; Tam and Ho, 2005). We simulated situations with 1,3 or 5 recommendations. These levels were consistent with managerial practices andwith past research studies. For instance, Tam and Ho (2005) compared situationspresenting 3 vs 6 recommendations.

Our manipulations on the type and number of recommended items were pre-testedamong experts and students. Experts judged the recommendations to be managerially

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relevant and realistic. Students had to participate in the pre-test session in the sameconditions as would be with the main experiment. After the second visit, they had torecall the items presented in the “suggestions” column. Most of them were able to sayhow many recommendations were presented and to infer why such items wererecommended (we got answers such as “because I bought a book by this author” or“because I like thrillers”).

Our final sample consisted of 371 participants (53 per scenario). The agedistribution was the following: 22 per cent were between 15 and 24, 44 per cent werebetween 25 and 34, 26 per cent were between 35 and 49 and 8 per cent were over 50.Perhaps, due to the nature of the task (repeated visits to a commercial web site sellingbooks, CDs and DVDs), our sample was biased in favour of women who represented 78per cent of our participants. All participants in our final sample were highly motivatedfor the task, since we did not consider responses from participants whose visits lastedless than five minutes.

Each visit ended with a questionnaire measuring participants’ appreciation of theweb site. Constructs were measured by multi-item seven-point Likert scales, except forantecedents of attitude towards the web site which were measured through seven-pointsemantic differential scales (Chen and Wells, 1999). Two scales for perceived benefitsand liabilities had been developed according to Churchill’s (1979) paradigm. Otherscales were taken from previous research and translated when necessary: attitudetowards the web site (Chen and Wells, 1999), attitude towards personalization(Holbrook and Batra, 1987). All scales are presented in the Appendix.

Validation of the conceptual frameworkOur conceptual framework proposed a set of antecedents and consequences for attitudetowards online personalization. All our constructs were latent variables, measuredthrough multi-item scales. Therefore, we used structural equation modeling to test thepredicted relationships (Chin, 1998).

Before testing our hypotheses, we checked the reliability and validity of the specificconstructs of our model. For perceived benefits, reliability was high as Cronbach’s awere between 0.71 and 0.80 and Joreskog’s r were between 0.72 and 0.81. We thenassessed convergent and discriminant validity. All factor loadings were significantand the average variance extracted (AVE) for each variable exceeded 0.5, meaning thatthe variance explained by the construct was larger than the variance due to the error(Fornell and Larcker, 1981). Discriminant validity was assessed using the procedureproposed by Fornell and Larcker (1981). The average extracted variances were greaterthan the squared correlations for all pairs of constructs. We carried out the same testsfor perceived liabilities, and the antecedents of attitude towards the web site,concluding as to their convergent and discriminant validity as well.

We first replicated the traditional view of antecedents of attitude towards the website (AWS): information (b ¼ 0.61; p ¼ 0.000), entertainment (b ¼ 0.24; p ¼ 0.003) andorganization (b ¼ 20.12; p ¼ 0.001). These three variables explained 78 per cent ofAWS variance and model fit was quite satisfactory (GFI ¼ 0.85; AGFI ¼ 0.80;RMSEA ¼ 0.03; TLI ¼ 0.94; IFI ¼ 0.95; CFI ¼ 0.95; CMIN/DF ¼ 2.18; P ¼ 0.00).H1a-H1c were therefore validated.

Our conceptual model proposed that attitude towards personalization (AP) shouldbe considered as an additional antecedent to attitude towards the web site. When we


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41

added AP in the preceding model, the proportion of AWS explained variance increasedsignificantly (R 2 ¼ 0.82) with an acceptable adjustment fit (GFI ¼ 0.84;AGFI ¼ 0.80; RMSEA ¼ 0.03; TLI ¼ 0.95; IFI ¼ 0.95; CFI ¼ 0.95;CMIN/DF ¼ 1.86; P ¼ 0.00). H3 was therefore validated. More importantly, AP hada positive effect and was the second most important antecedent (b ¼ 0.195; p ¼ 0.000),after information (b ¼ 0.564; p ¼ 0.000), and before entertainment (b ¼ 0.167;p ¼ 0.033) and organization (b ¼ 20.078; p ¼ 0.016). H2 was therefore validated.Regression weights and formal testing for H1 and H2 are presented in Table II.

The H4 group of hypotheses was tested through the structural coefficients underAMOS. All hypotheses were rejected, except for the H4b and H4f. We found a positiveeffect of perceived financial benefit on attitude towards personalization(non-standardized b ¼ 0.357; p ¼ 0.001) and a negative effect of quantity liability onattitude towards personalization (non-standardized b ¼ 20.146; p ¼ 0.035).

However, the three perceived benefits as well as the three perceived liabilities werepositively correlated. To avoid misleading collinearity problems, correlation analysiswas performed with a mean score for each dimension (Table III). All perceived benefitshad strong positive correlations with AP whereas all perceived liabilities had negativecorrelations with AP.

Observed effects of personalization on behaviorsTwo kinds of behavioral data were captured during the experiment: navigational dataand personalization effectiveness data.

Researchers in online marketing traditionally consider the number of pages visitedand the average visit length as indicators of web site interest (Huberman et al., 1998;Bucklin et al., 2002; Bucklin and Sismeiro, 2003; Senecal et al., 2005). Therefore, wecollected indicators such as visit length, number of pages visited and nature of thepages visited (category and subcategory). We measured the recommendation sourceeffectiveness by a dichotomous variable indicating whether the consumer eventuallybought the recommended item or not, as did Senecal and Nantel (2005).Before comparing consumers’ reactions to different scenarios, we checked whether therespondents’ characteristics differed or not across scenarios. Variance analyses

Structural pathNon-standardized

coefficient SD CR p-valueHypothesis

testing

Attitude towardsthe web site

ˆ information 0.564 0.073 7.744 – H1a validated


ˆ organization 20.078 0.032 22.416 0.016 H1c validated


ˆ personalization 0.195 0.031 6.221 – H2 validated


ˆ entertainment 0.167 0.078 2.134 0.033 H1b validated

Notes: Adjustment fit of the model: absolute indices – GFI ¼ 0.842, AGFI ¼ 0.801, RMSEA ¼ 0.030,RMR ¼ 0.186; incremental indices – TLI ¼ 0.947, IFI ¼ 0.954, CFI ¼ 0.954; parsimony indices –CMIN/DF ¼ 1.86

Table II.Structural paths

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Table III.Correlations between

perceived benefits andliabilities


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43

revealed that the distributions of age (x 2 ¼ 11.135; df ¼ 18; p ¼ 0.889) and gender(x 2 ¼ 3.352; df ¼ 6; p ¼ 0.763) were the same across scenarios. We also found nosignificant difference for additional individual variables measured. Therefore, wecould attribute differences in behaviours to our different manipulations and not toindividual differences among scenarios.

Table IV shows the average visit length and the average number of pages visitedduring the two visits. We first observed that visit length and number of pages visitedboth decreased between the first and the second visit. That could reflect a “learningphenomenon”. We also found that the second visit to the web site was marginallylonger for the personalized merchandising than for the random merchandising(m ¼ 823 vs m ¼ 687; p ¼ 0.051), whereas the number of pages visited was notsignificantly different (m ¼ 36 vs m ¼ 38; p ¼ 0.262). This implied that personalizationcould increase time spent on each page.

Our behavioral data confirmed that personalized merchandising was more efficientin terms of clicks than random merchandising (Table V). Click levels, however,remained quite low (Hussherr, 1999).

We observed that the standardized total of clicks (clicks divided by the number ofitems suggested) was higher for “close” recommendations (scenarios 1-3) than for“broad” ones (scenarios 4-6). Additionally, scenarios with fewer items presented led tobetter standardized totals. When we looked in more detail at the average perceivedbenefits and liabilities for scenario 2 (three close recommendations), we could see thatthis very favorable reaction was associated with the highest level for the experientialbenefit (5.31 on a seven-point scale) and the lowest commercial liability (2.90 on aseven-point scale).

Random Personalized t-stat. Sig. (unilateral)

Visit length (second) – Visit 1 1,039 1,079 20.356 0.361Number of pages visited – Visit 1 51 57 21.085 0.140Visit length (second) – Visit 2 687 823 21.637 0.051Number of pages visited – Visit 2 38 36 0.615 0.262

Table IV.Characteristics of thevisits

Click Basket insertion

ScenarioType of itemspresented

Number of itemspresented Total

Standardizedtotal Total

Standardizedtotal

1 Close 1 11 11.0 9 9.02 Close 3 21 7.0 16 5.33 Close 5 21 4.2 16 3.24 Broad 1 3 3.0 0 0.05 Broad 3 8 2.7 8 2.76 Broad 5 11 2.2 8 1.607 Random 5 7 1.4 3 0.60Total 82 60

Table V.Comparison of behavioralmeasurements

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Contributions and future research directionsPersonalization has become a widely adopted strategy on the internet. It is supposed toincrease customer retention “simply by making loyalty more convenient for thecustomer than non-loyalty” (Holland and Baker, 2001, p. 39). However, the literature ismore mitigated about personalization effectiveness. For instance, concerns aboutprivacy may contribute to negative reactions to personalization (Bitner et al., 1990;Treiblmaier et al., 2004).

Our contributions are of three types: theoretical, methodological and managerial.From a theoretical point of view, we propose a conceptual framework to understandconsumers’ reactions to online personalization. The methodological contributionrelates to our longitudinal experiment, which offers a valid comparison approach totest personalization effectiveness. Finally, our different scenarios enable us to providesome guidelines for managers.

No general framework for consumers’ reactions to online personalization wasproposed in the past (Ho, 2006). Based on a literature review and on a qualitativeapproach, we proposed specific antecedents and consequences for attitude towardsonline personalization.

We identified three perceived benefits and three perceived liabilities which explainconsumers’ attitude towards online personalization. Online personalization createsvalue for the customer by:

(1) reducing the time and effort needed in the decision-making process;

(2) helping find a better match for the customer’s preferences; and

(3) enjoying the discovery of new items.

These perceived benefits parallel those found by Sunikka et al. (2007) in the bankingsector. Similarly, attitude towards online personalization is also negatively related tothree perceived liabilities. Annoyance with the commercial aspect of personalizationdecreases a positive attitude towards personalization, as well as giving the feeling thatrecommendations are of poor quality or are too numerous. Again, these perceivedliabilities reflect previous results from literature on privacy (Phelps et al., 2000) and onelectronic agents (Wallet-Wodka, 2003).

Building a positive attitude towards personalization is important for web sitemanagers as our conceptual framework demonstrates that it as an antecedent of attitudetowards the web site, in addition to information, organization and entertainment (Chenand Wells, 1999; Chen et al., 2002; Muller and Chandon, 2004). Additionally, wemeasured intention to return to the web site in the future and this was highly correlatedwith attitude towards the web site. We preferred not to include it in our framework fordiscriminant validity purposes. However, a positive and strong attitude towards the website may be used as a proxy for intention to return to the web site in the future.

From a methodological point of view, our research offers a contribution tolongitudinal research (Bergeron, 2001; Ho, 2006; Miceli et al., 2007). Indeed, in histheoretical review of online loyalty, Bergeron (2001) underlined the inherent limitationsof cross-sectional studies:

[. . .] our research was based on a descriptive perspective in a cross-sectional approach, andnot in a longitudinal approach, which limits our understanding of the long-term impact of thedifferent factors in our proposed model.


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Therefore, our longitudinal experiment enabled us to test the impact of onlinepersonalization with an appropriate methodology.

Previous studies on personalization issues suggested coupling behavioral data withattitudinal data. For instance, Tam and Ho (2003) compared downloading behavior andsatisfaction towards the music items for both the personalized and non personalizedgroups. Similarly, we collected browsing behavior data (visit length, number ofconsulted items, number of subcategories and items put in the shopping cart) as well asdeclarative data at the end of each visit (perceived benefits, perceived liabilities,attitude towards the web site, intention to visit the web site again, etc.). Therefore, wewere able to establish the positive effect of online personalization through both ahigher click-rate and a more favorable evaluation of the web site.

Finally, our research offers insights about two managerial questions: which productshould be recommended to a customer? How many recommendations should bepresented to an online visitor? These questions were raised by Murthi and Sarkar(2003) who presented a review of research studies that dealt with personalization.

Which product should be recommended to a customer? We compared consumers’reactions to three types of recommended items: random, “close” and “broad”recommendations. Personalized recommendations generated more clicks and moreinsertions in the shopping cart than random ones. One explanation might be thatpersonalized recommendations, which correspond to the customer’s personalpreferences, may remind him/her of the product. The probability that the productmay be included in the consideration set increases as well as the probability that itmight be purchased (Hauser and Wernerfelt, 1990).

This positive effect of personalized recommendations on behaviors is an importantcontribution, as Fan and Poole (2006, p. 180) stressed that “empirical studies that havecompared and contrasted the effectiveness of different personalization technologies arerare”. Additionally, attitudinal measures confirm that “close” recommendationsgenerate greater benefits that “broad” ones, the best scenario being three “close”recommendations.

How many recommendations should be presented to an online visitor? A firstinsight was given by Tam and Ho (2005) who demonstrated that the size of therecommendation set (six vs three items) had a positive impact on elaboration and onchoice probability (47 vs 33 per cent). In our experiment, the size of therecommendation set could take three values: 1, 3 or 5 recommended items. Weobserved that visitors expressed higher concerns about the quantity liability whenthey were presented with five recommendations. Web site managers should thereforeavoid presenting too many recommendations.

Our research design presents some limitations. First, we chose “manageriallyrelevant” levels for the factors of our research design. Therefore, we compared 1 vs 5recommendations. In the second case, the quantity liability was higher, but thedifference was not significant. Had the manipulation been more marked, we wouldhave reached statistical significance. Second, our experiment guarantees internalvalidity but it lacks external validity. Indeed, visitors were asked to choose their fivepreferred items and put them in the basket. This procedure helped us “infer”consumers’ preferences. However, using this procedure again for the second visitprevented us from demonstrating the potential positive effect of personalizedmerchandising on basket size. In the sales promotion context, Gupta (1988) established

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that promotional variables do affect purchased quantity. In the same vein, personalizedmerchandising could increase cross-selling and the size of the average basket.

Fruitful research areas include considering situational as well as individualvariables. For instance, one could study online personalization effectiveness underhigh vs low situational involvement. Individual characteristics, such as familiarity andexpertise with the product category, could also influence the most efficient algorithm toimplement. For instance, we can imagine that expert consumers prefer “close”recommendations whereas novices prefer “broad” ones. Finally, complementary datacollection methods, such as eye-tracking techniques, could be used to get a completepicture of personalization effects. Indeed, we observed during some pre-tests thatrespondents stared longer at the personalized zone. This technique has already beensuccessfully used in advertising efficiency studies (Hussherr, 1999; Maughan et al.,2007).

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About the authorPauline de Pechpeyrou is an Assistant Professor at Institut du Management et de la Distributionin Roubaix (France), where she teaches online marketing, multi-channel retailing and salespromotions. She belongs to the Lille School of Management Research Center (EA 4112). Her mainresearch interests are retailing, e-business and sales promotions. Pauline de Pechpeyrou can becontacted at: [email protected]

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

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Table AI.Scales and items


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Documents

Pechpeyrou 2009 - How_consumers Value Online Personalization