The Role of Trust in Consumers’ Evaluations of Website Personalization
Pauline de Pechpeyrou, ESSEC Business School
Pierre Desmet, ESSEC Business School and Paris-Dauphine University
Abstract
Websites are trying to increase their sales through a personalized merchandising: they adapt the page content depending on the information available on the visitor’s known or inferred preferences. Such a strategy enhances customer value through three types of benefits (cognitive, financial and experiential). However it can also decrease value through three perceived liabilities (commercial aspect, quality and quantity of the personalized recommendations). The results of our research demonstrate that (1) trust in website personalization will drive the positive effect of perceived benefits and the negative effect of perceived liabilities on attitude toward personalization, (2) these benefits and liabilities are antecedents of a general attitude toward website personalization, (3) which is a new and important antecedent of attitude toward the website.
Purpose of the Research
Commercial websites have long been considered as an advertising media and traditional models of advertising persuasion have been successfully transposed to understand their effects (Karson and Fisher, 2005). Specifically, attitude toward the website (AWS) has a positive link with willingness to return to the website (Donthu, 2001; Desmond and Stewart, 2002) and can be considered as a strong antecedent of site loyalty.
Web personalization is used to generate individualized content for each visitor. Personalization techniques include content filtering, collaborative filtering, and a rule-based engine approach. It represents the most advanced step in online relationship marketing (Schubert and Ginsburg, 1999). Personalization requires collecting and using personal information to adapt the website to users’ needs and preferences (Pine and Gilmore, 1999). This collection of personal preferences enhances the crucial role of trust (Culnan and Armstrong, 1999; Jarvenpaa et al., 2000). Indeed, misuse of these data- mining technologies can have a major strategic impact on a company, damaging its reputation and limiting the amount of trust it can foster in relationships with customers (Bloom et al., 1994).
I n t his research we study merchandising personalization which consists in products recommendations depending on the information available on the visitor’s known or inferred preferences. The main research idea is that benefits and liabilities provided by merchandising personalization are antecedents of a general attitude toward website personalization which is a new and important antecedent of attitude toward the website. We also establish that trust in website personalization has a strong influence on these benefits and liabilities.
Theoretical Framework
The Theory of Reasoned Action (Ajzen and Fishbein, 1980) and the Technology Acceptance Model (Davis, 1989) underline the crucial role of beliefs to explain attitude toward an object.
Therefore, beliefs around online personalization are expected to explain attitude toward personalization and consequently attitude toward the website.
Personalization Generates Benefits and Liabilities
Ho and Tam (2005) have stressed that knowledge about consumers’ responses to online personalization is scarce: “Works in this area concentrate on computational procedures to sort out transactions and personal profiles. Although these studies look into various aspects of personalization applications, little attention has been paid to the theoretical basis for understanding the relationship between personalization and user behaviour” (p. 96).
Therefore we undertook a qualitative study to understand consumers’ perceptions and evaluations of website personalization. We chose semi- structured interviews because they are useful to determine motivations and attitudes (Evrard et al., 2003). Five main themes were discussed during the interviews: Internet usage, perception of commercial websites, experiences of online customization and personalization, attitude toward personal data collection and suggestions for websites improvement. Thirteen web users were interviewed, aged 24-51.
More than half of them had already experienced some personalization on the Internet. The examples they mentioned covered collaborative filtering as well as content filtering. However all respondents were asked to think about potential benefits and liabilities around web personalization.
Focusing specifically on value, this preliminary qualitative study concludes th a t a personalized online merchandising gives three types of benefits to the visitor: cognitive, financial and experiential.
Cognitive benefit from website personalization results from reduced search and comparison costs to find the best alternative. It derives from a recommendation of a shorter list of presumably most preferred products obtained, for example, through the processing of inferred attribute preferences. Indeed online personalization has been suggested as a way to reduce the amount of information the visitor has to deal when browsing on the website (Schubert and Ginsburg, 1999). Financial benefit refers to the reduction of perceived risk associated with a bad choice decision. Risk can be defined as “a subjective expectations of loss” (Stone and Grönhaug, 1993, p. 42). Global risk can be apprehended through one or two main dimensions (Volle, 1995). Stone and Grönhaug (1993) established in their research that financial and psychological risks were the predominant risk dimensions. When shopping online, the consumer mainly faces a financial risk if he doesn’t choose the best product according to his preferences. Swaminathan (2003) assumes that a recommendation agent is particularly useful when perceived risk is high. Experiential benefit refers to the discovery of unknown and surprising products that the visitor will like. This discovery should lead to value, according to the experiential perspective (Hirschman and Holbrook, 1982).
A personalized online merchandising can also create liabilities to the visitor. Three types of liabilities have been identified through the qualitative study: commercial annoyance, low perceived quality and quantity. Commercial annoyance refers to the visitor feeling that his freedom to browse the website is restricted and embarrassed by intrusive commercial messages. Literature on service marketing has underlined the “negative reactions of customers to unprompted and unsolicited employee behaviours” (Bitner et al., 1990, p. 81). In the same vein, our qualitative interviews have underlined that personalized suggestions are considered as “unsolicited commercial actions”. Low perceived quality for recommendations
should always be low as the website will never understand the true decisional logic and the true preferences of the visitor. Excessive quantity of suggestions refers to the perceived excessive pressure from too many messages sent. Indeed, websites greatly vary in the number of personalized recommendations they propose to visitors (Murthi and Sarkar, 2003; Tam and Ho, 2005, p. 277). In some cases, the consumer may feel overwhelmed by recommendations. The Central Role of Trust in the Formation of Beliefs
Trust is a central construct in the value chain that leads to consumer commitment toward the firm (Guibert, 1999). Most research works in marketing adopt a bi-dimensional conceptualization of trust: objective credibility of the partner groups competency and honesty attributions whereas benevolence attribution corresponds to the good and caring intentions of the partner. Benevolence dimension is not considered here for two reasons: honesty and benevolence are difficult to distinguish (Larzelere and Huston, 1980, p. 596) and the benevolence dimension raises many theoretical, methodological and managerial difficulties (Gurviez and Korchia, 2002).
Trust in website personalization is defined as “a psychological variable which reflects a sum of presumptions relative to the credibility and to the integrity of the website when personalizing” (Gurviez and Korchia, 2002). The credibility dimension refers to “the perceived performance of the website when personalizing its merchandising” whereas the integrity dimension refers to “the attribution of loyal motivations as far as promises of personalization are concerned”. We hypothesize that trust influences personalization perceived benefits and liabilities and so alters the attitude toward website personalization which finally drives attitude toward website and willingness to return.
The cognitive, financial and experiential benefits will only exist as far as the objective (choosing the best alternative) is surely obtained. The exploratory study underlines that this benefit greatly depends upon the perceived competency of the website to personalize: “Some websites collect this information to personalize the merchandising, but unfortunately, I perfectly know that they will do it in an improper way that doesn’t meet my need”. Therefore, we postulate: (H1) The perceived (a) cognitive, (b) financial and (c) experiential benefits from website personalization are higher when perceived trust in the website personalization is high (rather than low).
Perceived liabilities are higher when the website is perceived as not genuine in its personalization. Such a belief would arise if the visitor has the feeling that the website announces personalization but doesn’t suggest products according to the visitor’s personal preferences (Schafer, 2001). A recommendation system really needs to be customer-centric (Adomavicius and Tuzhilin, 2002, p. 314). Therefore, we postulate (H2) Perceived (a) commercial, (b) quality and (c) quantity liabilities from website personalization are lower when perceived trust in the website personalization is high (rather than low).
Attitude toward the website (AWS) is mainly explained by perceived usefulness (information and entertainment values) and perceived ease of use (organization) (Chen and Wells, 1999; Chen et al., 2002; Müller and Chandon, 2004). Because personalization has been shown to be a significant predictor of service quality (Mittal and Lassar, 1996) and satisfaction (Surprenant and Solomon, 1987), therefore we propose (H3) Attitude toward personalization (AP) has a positive effect on attitude toward the website (AWS).
Methodology
A commercial website with a fictitious brand name has been built by a professional working agency, offering cultural products (books, CDs and DVDs). Respondents (N=317), recruited on an on- line panel in Europe, have to make two visits separated by few days. Indeed, effectiveness of website personalization can only be measured through a longitudinal data collection protocol as the website has to learn visitors’ preferences (Ho, 2006; Micelli et al., 2007). During the first visit, respondents are asked to browse the website and to put their five preferred items in the shopping cart. For their second visit, the front page is adapted and contains personalized product recommendations based upon the attributes of the selected products. Respondents are asked to browse the website again. Website is evaluated twice and, after the second visit, respondents also have to indicate their agreement on several statements regarding the perceived benefits and liabilities, integrity and credibility dimensions associated with website personalization.
Constructs are measured with multi- item 7-point Likert scales except for antecedents of attitude toward the website measured through 7-point semantic differential scales (Chen and Wells, 1999). Two scales for perceived benefits and liabilities were developed according to Churchill’s paradigm (1979) with satisfactory scales reliability (Cronbach’s alphas over the .80 threshold). Other scales were borrowed from past research and translated when necessary: attitude toward the website (Chen and Wells, 1999), attitude toward personalization (Holbrook and Batra, 1987), credibility and integrity dimensions of website personalization (Gurviez and Korchia, 2002).
Findings AP as an Antecedent of AWS
A structural model confirms the three antecedents of AWS found in the literature: information (b=0.548; p=0.000), entertainment (b=0.246; p=0.003) and (des)organization (b=-0.112; p=0.001). These three variables explain 78% of AWS variance and model fit is quite satisfactory (GFI=0.86; AGFI=0.81; RMSEA=0.04; TLI=0.94; IFI=0.95; CFI=0.95; CMIN/DF=2.18; P=0.00).
Adding attitude toward personalization as an additional antecedent to AWS significantly increases the proportion of AWS explained variance (R²=0.82) with an acceptable adjustment fit (GFI=0.84; AGFI=0.80; RMSEA=0.03; TLI=0.95; IFI=0.95; CFI=0.95; CMIN/DF=1.86; P=0.00). More importantly, AP has a positive effect and is the second most important antecedent in size effect (b=0.195; p=0.000), after information (b=0.564; p=0.000), and before entertainment (b=0.167; p=0.033) and (des)organization (b=-0.078; p=0.016). Therefore, structural equation modelling with AMOS establishes that attitude toward personalization is an important antecedent of attitude toward the website.
Benefits and Liabilities as Antecedents of AP
Benefits and liabilities are strongly correlated and to avoid misleading collinearity problems, correlation analysis is presented with mean score for each dimension (see table 1). All perceived benefits have strong and positive correlations with AP whereas all perceived
liabilities have negative correlations with AP. These correlations with AP are all significant at the 0.000 level.
Table 1 – Correlation Analysis on Perceived Benefits and Liabilities
Cognitive Financial Experiential Commercial Quality Quantity AP
Cognitive 1 Financial 0.559** 1 Experiential 0.574** 0.427** 1 Commercial -0.286** -0.208** -0.299** 1 Quality -0.412** -0.420** -0.265** 0.597** 1 Quantity -0.082 (0.142) -0.100 (0.076) 0.051 (0.369) 0.279** 0.268** 1 AP 0.420** 0.438** 0.356** -0.263** -0.319** -0.172** 1
Relationship between Trust and Benefits and Liabilities
T h e s tructural model linking perceived benefits/liabilities and trust has an acceptable adjustment fit (RMSEA=0.08; TLI=0.90; IFI=0.92; CFI=0.92; CMIN/DF=3.25; P =0.00). It shows that trust has, as hypothesized, a positive influence on benefits and a negative influence on liabilities. All relationships are in the expected direction and significant at the 0.000 level except for the effect of trust on quantity liability (p=0.074). Stronger effects are found for cognitive (b=0.76; p=0.000) and financial benefits (b=0.73; p=0.000), then for experiential benefit (b=0.57; p=0.000). Trust has a lower effect on liabilities, the highest being doubt on recommendations quality (b=-0.49; p=0.000), then commercial pressure (b=-0.38; p=0.000) and finally quantity liability (b=-0.05; p=0.074).
Discussion
Firms are increasingly using website personalization as it is now technically feasible at a rapidly decreasing cost. Yet personalization efficiency is still an open question as visitor’s attitude toward website personalization is ambiguous, resulting from various perceived benefits and liabilities. Cognitive and financial benefits are the two most important ones, providing lower search and comparison efforts and lower perceived risk. Experiential benefit from discovering unexpected but adequate alternatives seems to be the least important benefit. Symmetrically, liabilities and doubts emerge: the most important is the doubt about recommendations quality. Additionally to the process performance, visitor’s specific variables (variety seeking, context variations, user/buyer differences) can cause bad quality recommendations. A second group of liabilities is related to an excessive promotional pressure either by introducing too much personalization in the website content or by sending too many messages (commercial annoyance).
We have shown that these benefits and liabilities are building blocks of the attitude toward website personalisation and that this attitude is an important determinant of attitude toward the website. In size, this effect is second after the information value of the website.
Our empirical results confirm the important role of trust in consumers’ evaluations of online personalization. Indeed all benefits are increased when trust is high whereas all liabilities are reduced. Therefore, for online personalization to be truly efficient, websites should develop trust in the personalization. Additional research has to be done to consider the antecedents of trust in the personalization process. Obviously trust in the website should be a strong antecedent but the relationship cannot be studied here, the website being fictitious. More
tactically, communication explaining why some items are suggested and clues inducing trust in the process must be studied. One step in that direction is Amazon’s feature: “Why we suggest you that article”, which tells the visitor the reason why the website recommends a specific item.
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