Word of Mouse: The Role of Cognitive Personalization in Online Consumer Reviews 

Lan Xia, Nada Nasr Bechwati

Bentley College


This research attempts to understand the mechanisms underlying the differential impacts of online consumer reviews, using the concept of cognitive personalization. In two experiments, the authors show that the level of cognitive personalization developed while reading an online review influences consumers’ purchase intentions. The level of cognitive personalization is a function of the reader’s affect intensity, the nature of the product reviewed (experience vs. search), and the content of the review (experiential vs. factual); in addition, the effect of cognitive personalization on purchase intention is moderated by valence (positive vs. negative).

Keywords: Word of mouse, online reviews, personalization.


Most business Web sites display product reviews written and posted by consumers. Although it is rare to find an e-commerce site without consumer reviews, little research has focused on what makes a review influential. With the explosive growth of e-commerce, online word of mouth (or word of mouse) has started to attract the attention of both researchers and practitioners (Bickart and Schindler 2001; Brown, Broderick, and Lee 2007; Dwyer 2007). Research demonstrates that word of mouse influences consumer online shopping behaviors (Bickart and Schindler 2001) or may be used as a heuristic in information search (Smith, Menon, and Sivakumar 2005). However, few researchers examine what makes certain reviews more influential than others.

This research investigates factors that may influence the impact of consumers’ reviews on readers. We propose that the effect of a review is contingent on the level of cognitive personalization of the reader. We define cognitive personalization as the extent to which readers find resonance in the review and think about how they would feel in a situation described in the review. Several factors influence the level of cognitive personalization, including individual characteristics such as the reader’s affect intensity, the nature of the product, and the content of the review.

In this research, we focus on posted reviews, the most frequently used forms of online word of mouth (Schindler and Bickart 2005). Because of the potentially extreme and detrimental influence of negative information (Basuroy, Chatterjee, and Ravid 2003), consumers’ reactions to positive and negative reviews may not be the same. We therefore examine positive reviews first, then discuss the potential differences between positive and negative reviews.

Conceptual Framework

Word of Mouse vs. Word of Mouth

Research shows that word-of-mouth (WOM) communication plays a more important role than market-generated information, such as advertising or personal selling, in influencing consumer behaviors (e.g., Katz and Lazarsfeld 1955), because consumers perceive WOM as more trustworthy, which may lead to greater persuasiveness (Wilson and Sherrell 1993). Moreover, research shows a tendency among people to trust and agree with those whom they like (Chaiken 1987). With WOM, cues such as the relationship with the information seeker or biological or social commonality between information seeker and the source can be used to develop a sense of trustworthiness (Gilly et al. 1998; Rogers 1983).

How do consumers determine whether to trust online reviewers and their reviews? Word of mouse differs from traditional WOM in that sources of information are individuals who have little or no prior relationship with the information seeker. Online, there is no apparent cue that the information seeker can use to develop a strong versus weaker tie distinction. The only source from which readers can draw inferences about trustworthiness and usefulness is the review itself. Drawing on research into offline WOM, we expect that when reading an online review, if a reader senses resonance with the reviewer and can imagine him- or herself in the situation described in the review, he or she may perceive the review as more credible and trustworthy and hence more persuasive. Therefore, we propose that cognitive personalization is essential in determining the influence of the review.


We define personalization as a cognitive operation by which readers of a review think about and feel as if what the review describes has actually happened to them (Larsen, Diener, and Cropanzana 1987). Personalization, as we define it, thus is a mental operation initiated by the reader and elicited by the external stimuli. In a broader sense, personalization also may refer to a tailored environment, product, service, or technology (Murthi and Sarkar 2003). Thus, personalization could mean the deliberate decoration or modification of the environment to fit a person’s tastes or preferences, such as decorating one’s own room or engaging in a process that changes the functionality, interface, information content, or distinctiveness of a product, service, or system such as a Web site interface. The purpose of personalization is to increase the element’s personal relevance to an individual (Blom 2000). Research shows that consumers welcome personalized product offerings and that personalized messages enhance responsiveness (Howard and Kerin 2004). Consumers perceive personalized offers as more relevant to their needs and better aligned with their preferences, which in turn enhances their purchase intentions.

Our concept of cognitive personalization differs from the traditional concept of personalization, which is primarily physical (Blom 2000). Cognitive personalization is a mental operation that takes place in a person’s mind. In the medical field, Beck (1976) defines personalization as a cognitive operation by which people interpret events in a self-referential manner. Beck addresses certain disorders that cause people to practice self-referential thinking excessively; in our definition, we refer to self-referential thinking that occurs as a result of emotional resonance with a message (Larsen, Diener, and Cropanzana 1987).

Our conceptualization of cognitive personalization also differs from the concepts of personal relevance or perspective taking. Personal relevance is based mainly on the similarity between the reviewer and the reader and/or between the situation described by the reviewer and that anticipated by the reader. A review could be highly relevant if the reviewer fits the same profile as the reader or describes a situation with which the reader is familiar. However, the reader might not experience high levels of cognitive personalization if he or she does not feel an emotional resonance with the review. Cognitive personalization also differs from perspective taking, which has been conceptualized as the ability to recognize and understand the thoughts and feelings of others (Rushton 1980). A typical perspective-taking instruction asks participants to pay attention to the other person’s thoughts and feelings (Oswald 1996). Empathy and altruistic behavior frequently appear as the associated consequences of perspective-taking activities (Oswald 1996; Underwood and Moore 1982). Therefore, perspective taking can be described as putting oneself in another person’s position and thinking or feeling on this person’s behalf. In contrast, cognitive personalization involves thinking about how oneself would feel upon exposure to other people’s experiences. Research reveals that imagining how another feels (in a negative situation) and imagining how one would feel if in another’s situation are different and have different emotional consequences (Batson, Early, and Salvarani 1997). The former evokes empathy, whereas the later evokes both empathy and distress. Although empathy is closely related to the tendency to help, distress may influence other decisions.

We propose that cognitive personalization initiated by the reader is essential in determining the influence of an online review. When a customer reads an online review and processes the information in a self-referential manner, he or she may perceive the message as credible, valid, and trustworthy. Previous WOM research suggests that trustworthiness enhances perceived usefulness (Wilson and Sherrell 1993). Therefore, cognitive personalization may enhance preferences for the product and responsiveness to the message (e.g., Howard and Kerin 2004). When a reader perceives a message as useful, he or she tends to give it more weight, which produces a greater impact on purchase intentions, whether positive or negative. Therefore, we posit:

H1:  A higher level of cognitive personalization when reading a positive (negative) online review leads to higher (lower) purchase intentions.

H2:  The effect of personalization on purchase intentions is mediated by perceived usefulness of the review

Antecedents of Cognitive Personalization

The level of cognitive personalization experienced by a consumer reading an online review depends on a variety of factors. We discuss three factors and their relationships with personalization: (1) affect intensity, an individual trait; (2) type of review, or experiential versus factual; and (3) the nature of the product, namely, search versus experience goods.

Affect intensity. Affect intensity (AI) is an individual difference that refers to people’s emotional responses to various events (Larsen and Diener 1987). People with high AI are more likely to be bursting with joy in happy situations and sense the end of the world when facing a disconcerting problem. Those with relatively higher AI also are more likely to respond to an event with more intense emotions and be responsive to a wider spectrum of emotions, both positive and negative.

The concept of AI is important in understanding how consumers respond differently to marketing stimuli (Escalas, Moore, and Britton 2004). For example, research on TV advertising suggests that high AI persons tend to experience higher levels of emotional enjoyment in response to positive emotional appeals and express more positive attitudes toward the stimuli (Moore and Harris 1996). In an online environment, the AI construct may also explain why people react to the same online review differently. By definition, consumers with higher AI should experience more emotional resonance with an online review. Research examining the underlying mechanisms of the influence of AI suggests that cognitive personalization represents one of the cognitive operations of AI (Larsen, Diener, and Cropanzana 1987). Affectively reactive persons tend to “overestimate the degree to which events are related to them and to be excessively absorbed in the personal meanings of particular happenings” (Beck 1976, pp. 91-92). Therefore, we expect the level of AI to be positively associated with the level of cognitive personalization.

H3:  Higher levels of AI will lead to higher level of cognitive personalization

Type of review. The extent to which information seekers sense resonance with a review may depend on the nature of the review. In a review, consumers look for cues that suggest validity (Schindler and Bickart 2005). With no apparent demographic or background information about a reviewer, such cues likely come from the content of the review, which might be predominantly factual or experiential. Factual reviews focus on plain facts, such as product attributes, whereas experiential reviews may focus on the reviewer’s own specific experience when buying or using the product.

Which type of review leads to higher levels of personalization? By definition, experiential reviews are likely to be more vivid than factual reviews. However, their effects may vary according to individual consumer differences. Research shows that the effect of AI only becomes manifest in situations in which stimuli are emotionally laden (Moore and Harris 1996). When the stimuli are not emotional in nature, there should be no difference between high and low AI persons. Therefore, we suggest the following interaction effect of AI and type of review:

H4:  Higher AI leads to a higher level of personalization when the review is experiential, whereas no such effect occurs when the review is factual.

Nature of the product. Products can be classified as search or experience goods (Nelson 1970). Search goods, such as electronics, are products that consumers can evaluate according to their specific attributes before purchase. Experience goods, such as recreational services, vary across consumers and are difficult to describe using specific attributes. Experience goods thus typically must be evaluated by affective evaluative cues (i.e., aesthetics of the product), whereas search goods may be evaluated by instrumental evaluative cues (i.e., technical or performance aspects of a product) (Ben-Sira 1980).

Research on WOM shows that the greater the importance of affective evaluative cues, the greater the likelihood that consumers seek out strong tie sources for recommendations. However, strong tie recommendation sources are relatively less important as sources of information for instrumental cues (Duhan et al. 1997). Hence, we posit that

H5:  Reviews for experience goods lead to higher cognitive personalization than reviews for search goods.

Negative Reviews

Consumers encounter both positive and negative reviews when they shop online. Research demonstrates the existence of a negativity bias, such that negative information tends to have a greater impact than positive information (Kahneman and Tversky 1984) because receivers perceive it as more diagnostic. For example, negative reviews hurt a movie’s box office performance more than positive reviews help it (Basuroy, Chatterjee, and Ravid 2003). Consumer information search research also notes that when a time constraint exists, people tend to focus more on negative than on positive information (Wright 1974). Therefore, we propose

H6:  Negative reviews lead to higher level of personalization than do positive reviews.

However, though negative information draws more attention than positive information, it might not have greater level on purchase intentions than positive information. People tend to resist the persuasion of negative information when their prior belief is stronger or their commitment to their choices is high (Ahluwalia 2000). In the context of online reviews, people tend to use online reviews and feedback when they have sufficient interest in an item (Weinberg and Davis 2005), that is, when their preferences likely have been somewhat formed. At this stage of decision making, positive information confirming a preference should boost purchase intentions. However, one negative review is not sufficient to motivate consumers to drop their preferences, especially when the target product is a search good for which a great amount of information is available and another consumer’s opinion does not count much. Therefore, we hypothesize that

H7:  Level of personalization has a greater impact on purchase intentions when the review is positive than when it is negative for search goods.


We conduct two studies to test these hypotheses. The first study focuses on positive reviews, whereas the second study examines negative reviews. In both studies, a digital camera and an airline ticket serve as examples of search and experience products, respectively. We chose these two examples after extensively reviewing various online review sites, on which electronics and travel draw the most reviews. In addition, our participants (students) are likely to have some degree of familiarity with both categories. A digital camera is a clear example of a search goods, and though purchases of airline tickets may be based on attributes such as price, departure time, and number of stops, the actual services, such as in-flight amenities or baggage handling, are intangible and cannot be evaluated before consumption. Participants were told that both airline choices were similar and comparable in terms of their searchable attributes and that the reviews provide information about the intangible aspects of their services. 

Study 1 Method

Eighty-five undergraduate students participated in a 2 × 2 mixed factorial design experiment. We manipulate type of review (experiential vs. factual) as a between-subjects factor and type of product (airline ticket and digital camera) as a within-subjects factor. In the experiential condition, the reviews focus on the reviewers’ own experiences and contain context-specific claims, such as that the product/service was superior. The factual reviews consist of a set of facts and product attributes (see the Appendix). To ensure realism, we adapt real online reviews with minor modifications.

Participants were told that they were planning a trip to visit friends/family during their winter break and needed to both book a ticket and buy a digital camera to take pictures during their trip. After some information search, they had already narrowed their choices to two options in each purchase category. The two airlines flew the same route for similar prices, and the two brands of digital cameras offered comparable prices and features. Participants then read some online reviews to help them make their final decisions. Each participant read two reviews, one for an airline and one for a digital camera, with the order of the reviews rotated between subjects. After reading each review, participants answered a set of questions about the perceived usefulness of the review, their purchase intentions, level of cognitive personalization, and AI. In addition, we measured their online shopping experience, general attitude toward online reviews, and knowledge and interests in airlines and cameras, using seven-point Likert-type scales (see Table 1). We adapt our three-item cognitive personalization measure from Larson, Diener, and Cropanzano (1987). For AI, we adopt the 15-item scale used by Escalas, Moore, and Britton (2004), which is based on Larsen, Diener, and Cropanzano’s (1987) original measure. The scale items appear in Table 1. We also measure interest in and knowledge about the two products and use them as covariates; however, these results show no impact, so we drop them from further discussion.

Table 1. Scales and Reliability

  Airline Camera
Personalization α = 0.81 α = 0.62
As I read the review, I thought about how I would feel if my friends, family, or I were in that situation. Study 1: α = 0.84

Study 2: α = 0.81

Study 1: α = 0.74

Study 2: α = 0.72

While reading the review, I was thinking about my own emotional reactions
As I read the review, I kept on thinking about how I would feel if the same thing happened to me
Perceived usefulness of the review α = 0.90 α = 0.95
This review is very useful in my decision making Study 1: α = 0.91

Study 2: α = 0.81

Study 1: α = 0.95

Study 2: α = 0.94

I find this review very helpful
This review will have a lot of influence on my preferences
Purchase intention α = 0.84 α = 0.95
It is very likely that I will buy the Kodak digital camera Study 1: α = 0.90

Study 2: α = 0.81

Study 1: α = 0.95

Study 2: α = 0.92

If I have to decide now, I probably will buy the Kodak digital camera
The likelihood that I will buy the Kodak digital camera is high
Affect Intensity (sample items)  
My emotions tend to be more intense than those of most people Study 1: α = 0.91

Study 2: α = 0.86

When I feel happy, it is a strong type of exuberance
My heart races at the anticipation of some exciting event.


Experimental checks. We check the manipulation of the type of review using two items: (1) “to what extent does the review focus on specific reviewer’s own experience rather than on generalization?” and (2) “to what extent does the review focus on specific product attributes?” The participants perceive the factual reviews as focusing more on specific product attributes compared with the experiential reviews (MFactual = 5.4 vs. MExperiential = 3.9, p < 0.001) but perceive the experiential reviews as focusing more on specific personal experience (MExperiential = 5.5 vs. MFactual = 4.5, p = .005). The presentation order of the reviews, online shopping experience, attitude toward online reviews, and knowledge and interest in the products have no effects on the major dependent variables; we therefore do not discuss them further.

Main results. We conduct separate analyses of the airline and camera data to examine the specific effects. Regression analyses reveal that cognitive personalization has a significant effect on purchase intentions for both camera (b = .56, p < .001) and airline (b = .46, p < .001) reviews, in support of H1. To examine the mediating effect of perceived usefulness, we follow the procedure to test mediation suggested by Baron and Kenny (1986), which indicates that a mediation effect exists when (1) AI directly influences perceived usefulness, (2) AI directly influences personalization, and (3) the direct effect of AI on perceived usefulness disappears when we add personalization as a predictor. The results in Table 2 demonstrate that for the airline reviews, when we add perceived usefulness to the regression (b = .70, p = .001), the effect of personalization is no longer significant (b = .07, p = .42). However, the effects are only partially mediated by perceived usefulness for the camera review. In addition, when we add perceived usefulness to the regression (b = .55, p = .001), the effect of personalization declines significantly but still statistically significant (b = .28, p = .002). Thus, we find partial support for H2.  

Table 2. Mediation Effect of Perceived Usefulness

Product Predictor Dependent Variable Beta p-Level
Airline (positive) Personalization (P) Purchase intention .46 .001
  Usefulness (U) Usefulness .56 .001
  Personalization (P) and Usefulness (U) Purchase intention P: .07;
U: .70
P: .42;
U: .001
Camera (positive) Personalization (P) Purchase intention .55 .001
  Usefulness (U) Usefulness .49 .001
  Personalization (P) and Usefulness (U) Purchase intention P: .28;
U: .55
P: .02;
U: .001
Airline (negative) Personalization (P) Purchase intention -.26 .02
  Usefulness (U) Usefulness .44 .001
  Personalization (P) and Usefulness (U) Purchase intention P: -.08;
U: -.40
P: .47;
U: .001
Camera (negative) Personalization (P) Purchase intention -.10 .38
  Usefulness (U) Usefulness .37 .001
  Personalization (P) and Usefulness (U) Purchase intention P: -.03;
U: -.18
P: .79;
U: .13

Next, we perform a median split on the AI scale and conduct a mixed factor ANOVA with type of review and AI as between-subject factors, type of product as the within-subject factor, and level of cognitive personalization as the dependent variable (we provide the means in Table 3). The results indicate a main effect of AI. Participants with higher AI report a higher level of cognitive personalization (MHigh = 3.89 vs. MLow = 3.39, F(1,81) = 4.74, p = .032), in support of H3. The main effect is qualified by an interaction with the type of review (F(1,81) = 5.44, p = .022; see Figure 1). Higher AI leads to higher level of cognitive personalization only when the reviews are experiential (MHigh = 4.06 vs. MLow = 3.03, F (1.40) = 8.77, p = .005). This result supports H4.

Figure 1. Effect of AI

We also find a main effect of product (Mairline = 4.05 vs. Mcamera = 3.23; F(1,81) = 19.83, p < .001). As we expected, participants exhibit a higher level of cognitive personalization with the airline reviews than the camera reviews. This main effect is qualified by an interaction with the type of review (F(1,81) = 18.24, p < .001). Factual airline reviews lead to a higher level of cognitive personalization than do experiential reviews (MFactual = 4.54 vs. MExperiential = 3.66, F(1,83) = 8.44, p = .005), but for cameras, experiential reviews create a higher level of cognitive personalization (MExperiential = 3.62 vs. MFactual = 2.93, F(1,83) = 5.49, p = .022).

In Study 2, we replicate Study 1 using negative reviews, with 82 students as participants. The other procedures and measures remain the same.

Study 2 Results

The regression analyses show that cognitive personalization has a direct impact on purchase intentions for airlines (b = -.26, p < .05) but no impact for cameras (b = -.10, p > .30), which offers only partial support for H1 in the context of negative reviews. The effect of personalization on purchase intentions in the airline context is completely mediated by the perceived usefulness of the review, consistent with H2 (see Table 2). The mixed factor ANOVA of the level of cognitive personalization indicates a marginal effect of the type of product (F(1, 78) = 2.62, p = .1). Consistent with H5, airline reviews create a higher level of cognitive personalization than do camera reviews (Mairline = 4.49 vs. Mcamera = 4.19). Participants with higher AI have slightly higher levels of personalization (Mhigh = 4.50 vs. Mlow = 4.15), but the effect is not statistically significant (p = .2). We do not observe an interaction between AI and type of review, though we find a small AI × type of review × type of product interaction (p = .1). The pattern of means (see Table 3) demonstrates that though there is not much difference in the level of personalization across AI levels and type of reviews for the airline, a recognizable interaction pattern emerges for the camera reviews. Participants with high AI show a similar level of personalization for factual and experiential reviews (Mfactual = 4.43 vs. Mexperiential = 4.59), whereas those with low AI indicate a higher level of personalization for experiential reviews (Mfactual = 3.26 vs. Mexperiential = 4.43, p = .8). This result may reflect the importance and diagnosticity that participants associate with negative reviews. Because negative information draws more attention and participants appear more motivated to personalize the information, this scenario greatly enhanced the level of personalization for low AI participants, whereas high AI participants may have approached their ceilings. Although this result does not provide further support for H4, it offers important information regarding the potential boundary of this effect.   

Table 3. Means for Cognitive Personalization

    Positive Review Negative Review
    Low AI High AI Low AI High AI
Airline Factual review 4.83 4.18 4.32 4.25
  Experiential review 3.04 4.38 4.57 4.74
Camera Factual review 2.81 3.67 3.26 4.43
  Experiential review 3.07 4.19 4.43 4.59


We pool the data from both studies to compare the effects of positive and negative reviews and find that the negative reviews lead to higher levels of cognitive personalization than do the positive reviews for the camera (F(1,165) = 17.1, p = .001), in support of H5. However, the effect is not significant for airline reviews (F(1,165) = 2.5, p = .11). Furthermore, higher personalization in the negative reviews condition does not translate into a greater impact on purchase intentions. A median split on the level of personalization, with an ANOVA analysis in which purchase intention serves as the dependent variable and personalization, type of review, and valence of the review are the independent variables, reveals an interaction effect between the level of personalization and valence for both products (camera F(1,159) = 11.9, p = .001; airline F(1,159) = 13.13, p < .01). For both products, higher personalization leads to higher purchase intentions when the review is positive (means = 4.0 vs. 2.8 camera and 5.0 vs. 4.1 airline). However, when the review is negative, level of personalization has no impact on purchase intentions for the camera review (mean = 2.5 vs. 2.9, p > .2) and a negative impact on purchase intentions for the airline (mean = 3.4 vs. 4.1, p < .05). Therefore, we find support for H7.


The results of Study 1, pertaining to positive reviews, confirm that higher levels of cognitive personalization prompt higher purchase intentions. In addition, this effect is primarily mediated by perceived usefulness. We find complete mediation in the airline review and partial mediation in the camera review scenario. The difference probably results from the nature of the product, in that evaluations of a search product are more objective, whereas those of search goods rely more on perceptions.

However, we find support for the effect of personalization only in the airline context in Study 2. As we expected, negative reviews draw more attention, and hence offer a higher level of personalization, but level of personalization does not influence purchase intentions for a search good. For a search good like a camera, plenty of additional information is available, so one negative review is probably not enough to deter consumers from a purchase, especially if they have considered many alternatives and narrowed their choices down to two options. An airline flight service is more experiential, and consumers lack sufficient information to validate the review claim. Hence, the more they perceive resonance with the negative review, the greater is its negative effect on their purchase intentions.

In terms of the antecedences of personalization, we confirm the main effect of type of product in both studies, though the effect in Study 2 is smaller. We also observe a main effect of AI and its interaction with type of reviews in positive reviews but not in negative reviews. As we have reasoned, this finding may be due to the enhanced motivation to personalize among low AI participants who face negative information.

We also observe an interaction between the type of review and type of product on the level of personalization in the positive review scenario. For search goods, experiential reviews create a higher level of personalization than do factual reviews, whereas for experiential goods, factual reviews initiate a higher level of personalization. Thus, the type of review that complements a particular type of product appears to provide greater resonance for readers. Search goods usually can be described by a set of standard attributes, so factual reviews likely do not offer additional information compared with what consumers can obtain from the manufacturer’s or retailer’s product descriptions. However, experiential reviews may convey concrete product attributes in a personal way, enabling readers to gain a sense of resonance with the situation described (i.e., personalize). Experiential goods lack standard attributes, and variation in product performance is not unusual; therefore, experiential reviews that focus on the reviewer’s own experience may be difficult to generalize, whereas the use of facts that back up an opinion may help readers construct their own consumption situations.

Finally, when we compare Studies 1 and 2, we find that negative reviews lead to a higher level of personalization than do positive reviews for cameras, in support of H5. However, higher personalization in the negative reviews condition does not lead to a greater impact on purchase intentions. Thus, it appears participants are more resistant to the persuasiveness of negative reviews, regardless of the level of personalization, but higher personalization enhances the effect of positive reviews on purchase intentions. We have argued that the differential impact of cognitive personalization on purchase intentions may result from a situation in which consumers turn to online product reviews when they already have a target product in mind. The description of the purchase task in our scenarios also may reinforce this context, because it tells participants that they found the reviews only after they engaged in some searching and screening. Further research should examine whether the consumer purchase stage moderates the personalization-purchase intention linkage.  

Conclusions, Implications, and Limitations

As its main contribution, this research introduces the cognitive personalization concept as a means to understand the effects of online reviews. Our study shows that the differential effect of online reviews is due partly to readers’ (i.e., information seekers’) cognitive personalization. When information seekers sense resonance with the reviewer, they perceive the review as more trustworthy and useful and give it greater influence over their purchase intentions. We demonstrate the key role of cognitive personalization in the case of an experience product (airline) and a search product (camera). However, we note that this effect is moderated by the valence of the review.

Furthermore, our research shows that the level of cognitive personalization is a function of the nature of the product and the content of the review, as well as the reader’s level of affect intensity. Overall, consumers perceive factual reviews as more useful than experiential reviews, consistent with existing research (Schindler and Bickart 2005). Moreover, persons with high AI tend to sense more resonance from a review with higher levels of cognitive personalization than do those with low AI when the review is emotionally laden in nature (i.e., experiential). 

By demonstrating that cognitive personalization is key to understanding the influence of a review, our research offers several important theoretical and managerial implications. Theoretically, our conceptualization and findings raise interesting questions related to how persuasion works. Consumers’ perceptions of online reviews and their consequent behaviors appear contingent on the level of cognitive personalization they experience. It is therefore worth investigating whether cognitive personalization represents a key mediator for persuasion techniques other than online reviews.

The key role of cognitive personalization, as supported by this research, also may have interesting practical implications for review writers and Web site managers. Consumers who actively post reviews may enjoy giving information and advice to other customers. For these consumers, finding that others perceive their reviews as helpful or influential may represent a reward for writing reviews. As our research shows, reviewers can enhance their influence by writing about specific product attributes in a way that enables readers to sense a feeling of resonance.

In addition, though reviews are typically written by peers and customers, manufacturers or retailers that publish these reviews may influence which reviews are posted and how the reviews are written. For example, a manufacturer’s Web site could post selected customer testimonies. Our research offers insights into what kinds of customer testimonies are more influential for certain types of product. Furthermore, retailers that post all customer reviews could offer guidelines or hints about how to write a review, which would enable more influential reviews.

Finally, our research contains several limitations that should be addressed by further research. First, we use products with which the participants are familiar to represent search and experiential products. However, many products have both search and experiential properties, and consumers may respond in different ways to different products. Therefore, reviews of other products should be examined as well. Second, our participants are college students, which may limit the generalizability of our results. Third, we employ a scenario-based approach, but a real purchase scenario may be more desirable for observing the effects of product reviews on consumer purchases. Fourth, we focus only on posted reviews in this research. Research should investigate whether cognitive personalization and its moderators have similar effects for other types of online word of mouse, such as private blogs. 


Ahluwalia, Rohini (2000), “Examination of Psychological Processes Underlying Resistance to Persuasion,” Journal of Consumer Research, 27 (2), 217-232.

Baron, Reuben M. and David A. Kenny (1986), “The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations,” Journal of Personality and Social Psychology, 51 (6), 1173-1182.

Basuroy, Suman, Subimal Chatterjee, and S. Abraham Ravid (2003), “How Critical Are Critical Reviews? The Box Office Effects of Film Critics,” Journal of Marketing, 67 (October), 103-117.

Batson, C. Daniel, Shannon Early, and Giovanni Salvarani (1997), “Perspective Taking: Imagining How Another Feels Versus Imagining How You Would Feel,” Personality & Social Psychology Bulletin, 23 (7), 751-759.

Beck, A.T. (1976), Cognitive Therapy and the Emotional Disorders. New York: International Universities Press.

Ben-Sira, Zeev (1980), “Affective and Instrumental Components in the Physician-Patient Relationship: An Additional Dimension of Interaction Theory,” Journal of Health and Social Behavior, 21 (June), 170-180.

Bickart, Barbara and Robert M. Schindler (2001), “Internet Forums as Influential Sources of Consumer Information,” Journal of Interactive Marketing, 15(Summer),  31-52.

Blom, J (2000), “Personalization-A Taxonomy,” CHI 2000 Conference on Human Factors in Computing Systems, ACM, New York, 313-314.

Brown, Jo, Amanda J. Broderick, and Nick Lee (2007),”Word of Mouth Communication within Online Communities: Conceptualizing the Online Social Network,” Journal of Interactive Marketing, 21 (3), 2-20.

Chaiken, Shelly. (1987), “The Heuristic Model of Persuasion,” in Social Influence: The Ontario Symposium, Vol. 5, M. P. Zanna, J. M. Olson, and C. P. Herman, eds. Hillsdale, NJ: Lawrence Erlbaum Associates, 3-39.

Duhan, Dale F., Scott D. Johnson, James B. Wilcox, and Gilbert D. Harrell (1997), “Influences on Consumer Use of Word-of-Mouth Recommendations,” Journal of the Academy of Marketing Science, 25 (4), 283-296.

Dwyer, Paul (2007), “Measuring the Value of Electronic Word of Mouth and its Impact in Consumer Communities,” Journal of Interactive Marketing, 21 (2), 63-79.

Escalas, Jennifer Edson, Marian Chapman Moore, and Julie Edell Britton (2004), “Fishing for Feelings? Hooking Viewers Helps!” Journal of Consumer Psychology, 14(1&2), 105-114.

Gilly, Mary C., John L. Graham, Mary Finley Wolfinbarger, and Laura J. Yale (1998), “A Dyadic Study of Interpersonal Information Search,” Journal of the Academy of Marketing Science, 26 (2), 83-101.

Howard, Daniel J. and Roger A. Kerin (2004), “The Effects of Personalized Product Recommendations on the Advertisement Response Rates: The ‘Try This, It Works!’ Technique,” Journal of Consumer Psychology, 14 (3), 271-279.

Kahneman, Daniel and Amos Tversky (1984), “Choice, Values, and Frames,” American Psychologist, 39, 341-350.

Katz, E. and P.F. Lazarsfeld (1955), Personal Influence: The Part Played by People in the Flow of Mass Communications. Glencoe, IL: The Free Press.

Larsen, R.J. and E. Diener (1987), “Affect Intensity As An Individual Difference Characteristics: A Review,” Journal of Research in Personality, 21, 1-39.

—, —, and R.S. Cropanzana (1987), “Cognitive Operations Associated with Individual Differences in Affect Intensity,” Journal of Personality and Social Psychology, 53, 767-774.

Moore, David J. and William D. Harris (1996), “Affect Intensity and the Consumer’s Attitude Toward High Impact Emotional Advertising Appeals,” Journal of Advertising, 25 (2), 37-50.

Murthi B.P.S and Sumit Sarkar (2003), “The Role of Management Sciences in Research on Personalization,” Management Science, 49 (10), 1344-62.

Nelson, P. (1970), “Information and Consumer Behavior,” Journal of Political Economy, 78 (2), 311-329.

Oswald, Patricia A. (1996), “The Effects of Cognitive and Affective Perspective Taking on Empathic Concern and Altruistic Helping,” Journal of Social Psychology, 136 (5), 613-623.

Rogers, Everett. M. (1983), Diffusion of Innovations, 3rd ed. New York: The Free Press.

Rushton, J. (1980), Altruism, Socialization, and Society. Englewood Cliffs, NJ: Prentice-Hall.

Schindler, Robert M. and Barbara Bickart (2005), “Published Word of Mouth: Referable, Consumer-Generated Information on the Internet,” in Online Consumer Psychology: Understanding and Influencing Consumer Behavior in the Virtual World, Curtis P. Haugtvedt and Karen A. Machleit (Eds.). Mahwah, NJ: Lawrence Erlbaum Associates, 35-61.

Smith, Donnavieve, Satya Menon, and K. Sivakumar (2005), “Online Peer and Editorial Recommendations, Trust, and Choice in Virtual Markets,” Journal of Interactive Marketing, 19 (3), 15-37.

Underwood, B. and B. Moore (1982), “More Evidence that Empathy Is a Source of Altruistic Motivation,” Journal of Personality and Social Psychology, 43, 281-292.

Weinberg, Bruce D. and Lenita Davis (2005), “Exploring the WOW in Online Auction Feedback,” Journal of Business Research, 58 (November), 1609-1621.

Wilson, W.R. and D.L. Sherrell (1993), “Source Effects in Communication and Persuasion Research: A Meta-Analysis of Effect Size,” Journal of the Academy of Marketing Science, 21 (Spring), 101-112.

Wright, Peter (1974), “The Harassed Decision Maker: Time Pressures, Distractions, and the Use of Evidence,” Journal of Applied Psychology, 59 (October), 555-561.

About the Authors

Lan Xia (Ph.D., University of Illinois Urbana-Champaign) is an Assistant Professor in the Marketing Department at Bentley College. Her major areas of research include consumer information processing,
behavioral pricing, and online consumer behaviors. Her work has appeared in Journal of Marketing, Journal of Retailing, Journal of Consumer Psychology, Journal of Interactive Marketing, and Journal of Product and Brand management. Email: [email protected].

Nada Nasr Bechwati (DBA, Boston University) is an Associate Professor in the Department of Marketing at Bentley College. Her research interests focus on the link between the purchase process consumers go through and their post-purchase behavior.  She has published, among other scholarly journals, in Journal of Marketing Research, Journal of Consumer Psychology, Journal of Interactive Marketing, Journal of Business Research, Journal of Business Venture, and the American Statistician. Email: [email protected]


Online Consumer Reviews Used in Studies 1 and 2

  Positive Reviews
  Experiential Factual
Airline When I was traveling with JetBlue for the first time, I had such a wonderful experience. The seat is comfortable, the snacks are wonderful, and I was thrilled with the direct TV. You can find such good services in nowhere these days. JetBlue is the best marketing concept in today’s airline world. This is about the 5th time that I used the airline. Even though I can fly first class for my business travel, I find myself searching to see if JetBlue flies that route… it will be cheaper for the company, and probably more pleasant for me. They’ve also become the most stylish brand in the market with their excellent TV ads. If you haven’t seen a JetBlue plane or commercial, don’t despair. They’re coming to an airport near you and determined to make flying fun again. On board JetBlue, the seats were a bit narrow, but leather and comfortable. The aircraft was an Airbus 320 configured in all coach seating 3 seats on each side of the aisle. Free snack selection was excellent: Blue Potato Chips, chocolate chip cookies, bagel/pretzel chips, animal crackers, nuts, Doritos and some others that I cannot recall. They were very good about giving extras as well. Beverage service was very comprehensive as well with no charge for sodas and juices and $3.00 for beer or wine and $4.00 for liquor. TV service worked great – kids got to watch Animal Planet and Cartoon Network and I was able to watch news, weather and sports. Offloading was quick and easy and the baggage arrived quickly – absolutely no complaints.
Digital Camera No camera is perfect, but the KODAK SC8500 comes very close. I’m happy with this camera. I’ve been using it for about a year. I have three kids and I have the camera with me whenever I play with them. It is easy to operate and takes wonderful pictures. It records all the precious moments that I will enjoy the rest of my life. If you can afford it, buy it. You will not be disappointed. This camera is currently the best pro-consumer digicam on the market and is well reviewed on other sites such as Steves-digicams. Bottom line: this is a serious amateur photographer’s camera that memorizes the best moments of your life. Kodak SC8500 takes great pictures, and reproduces colors very well. Image quality is decent: I’ve made prints of up to 8×10″ with it and it looks fine on a wall or a desktop. It offers a good combination of control of shutter speed (many options) and ‘aperture’ (3 choices, but lens really does 2.8-4.5). It is compact and has full manual control option. It adjust lights automatically and works good at night. It uses 4 standard AA batteries – a big plus. Fully charged, a set of 1200 mAh lasts for about 200 shots.



  Negative Reviews
  Experiential Factual
Airline Jet Blue is so hyped that I was curious to give it a try, and it was convenient for me since I am close to Long Beach Airport, one of Jet Blue’s airports. However, I was really unimpressed. Well let me tell you, I had my knees smashed the moment the person in the seat in front of me reclined his. I felt like I needed crutches after the flight. I’d much rather sit on cloth and actually be given some legroom and seat width. Throughout the flight, I had all these bright screens around and all this noise from the headsets–it is not exactly relaxing. In fact, if you can make it through a Jet Blue flight without aspirin, you’re a better person than I am. Next time, I will avoid JetBlue. Flying with JetBlue is not nearly as greatly as the airline claimed. First of all, there is extremely small leg room. If you’re considered even a remotely large person, JetBlue is probably not for you. Second, DirectTV is limited to only 24 channels with no premiums like HBO. Third, there is no plug under the seat (although they tell you there is one if you call) so you can’t use your laptop. Well, there’s barely any room to open it up and use it anyway. Fourth, the floor is dirty as if they don’t clean between flights. Fifth, offloading is not easy and takes a long time. Getting the baggage takes even longer. Bottom line: avoid JetBlue and fly with other airlines.
Digital Camera I purchased the Kodak SC8500 Digital Camera in May of this year and couldn’t wait to get it out of the box and take a picture. From the first picture I took I was terribly disappointed and it only got worse and, finally on August 14, I sent the camera back to Kodak. I have three kids and I have the camera with me whenever I play with them. I expect my camera to record all the precious moments that I will enjoy for the rest of my life. Unfortunately, this was not possible with this camera. I can’t seem to get all the pictures focused correctly. They look fine on the LCD but when downloaded, they really are not. Give the Kodak a second look if you are considering buying a digital camera. There are better ones out there. The Kodak SC8500 digital camera is OK except that the auto focus is horrible. It barely works in normal light and if the lighting is even slightly dim it won’t work at all. You might as well turn off your auto focus assist light because all it does is alert your subject you are about to shoot. It doesn’t help with the focus at all. The viewfinder is small and dark compared to a 35mm and there are no focus aids so you can’t even switch to manual and get it to work with adequate results. It is a good thing it is digital so you have real time feedback letting you know half your pictures are out of focus. Look past the ugly and bulky silver body and give the Kodak a second look if you are considering buying a digital camera. There are better ones out there.