The Internet has provided a competitive platform for online marketing and transactions. In particular, the use of the Internet as a venue for expressing opinions on products has become an important marketing tool to compete for consumer attention and visits (Chatterjee, 2001). It is estimated that the number of Internet users around the world exceeds 900 million while the information traffic doubles every 1 to 1.5 years (Kaynar & Amichai-Hamburger, 2008). Providing consumers with a platform to voice their opinions and monitoring its electronic word-of-mouth (WOM) activity has already become a viable business (Tedeschi, 1999). WOM is seen as more credible than advertising as it is perceived as having passed through the evaluation of “people like me” (Allsop, Bassett, & Hoskins, 2007). With the declining trust in advertising, WOM has become the most influential communication channel. As an electronic form of WOM, online consumer reviews provide a trusted source of product information for consumers and therefore a potentially valuable sales asset. Positive consumer reviews of products or companies are considered among the best predictors of business growth (Keller, 2007).

Kaynar and Amichai-Hamburger (2008) suggested two generalizations were to be found in previous work regarding the relationship between personality and the Internet. First, the behaviors of Internet users are different from their real world behaviors. Second, the Internet enables users to improve their psychological well-being through anonymity and a high degree of control. However, little is known about how online messages influence potential consumers’ evaluations of and purchasing intentions toward products (Chatterjee, 2001). The aim in the present study was to broaden understanding of online consumer behaviors. Need for cognition (Cacioppo & Petty, 1982) is the personality factor most commonly used as a means of defining and measuring individuals’ tendencies towards information (Amichai-Hamburger, Kaynar, & Fine, 2007), and, therefore, was adopted in the present study as a moderator in determining the influence processes of online reviews.

One theoretical perspective that can enrich understanding of the influence of online reviews is the elaboration likelihood model (ELM) developed by Petty and Cacioppo (1981, 1986). Park, Lee, and Han (2007) used the ELM to explore the mechanism of how online consumer reviews influence the attitudes of online shoppers. An experimental study was conducted to investigate the moderating role of involvement in determining the route to persuasion. They found that the quality and quantity of online reviews affect consumers’ purchasing intention (PI) but low-involvement consumers are affected by quantity rather than quality of online reviews and high-involvement consumers are affected mainly by review quantity when the review quality is high. Park and colleagues pioneered investigations into the influence process of online reviews, but their findings were not consistent with those of most of the studies of ELM. More research is required to better understand the effects of online reviews. Hence, the present study was aimed at extending the application of ELM into the emerging knowledge domain of online reviews. The contribution of the present study to social behavior literature in the context of electronic commerce is in the introduction of ELM as a referent theory.

Literature Review

Park and colleagues (2007) found that consumer satisfaction increases with the level of message quality, which leads to higher purchasing intention. This stream of research was focused on the argument quality of messages. Strong messages which are understandable and objective are considered more effective in changing attitude than are weak messages that demonstrate an emotional and subjective style (Petty & Cacioppo, 1984; Petty, Cacioppo, & Schumann, 1983). Online reviews that consist of understandable and fact-supported arguments are more persuasive than reviews expressing subjective feelings and emotional comments. In other words, a more favorable attitude will be formed when a higher quality online review is processed. Therefore, the following hypothesis was formulated:
H1: High quality argument in online reviews will have a positive impact on purchasing intention.

An online review is considered as a new form of recommender similar to the messenger of traditional word-of-mouth communication (Chatterjee, 2001). However, the contributors of online reviews are often prior users who wish to remain anonymous, while the sources of traditional word-of-mouth recommendations are often familiar people. Lack of credibility motivates online shoppers to use other cues in attitude formulation. The number of online reviews is often used to determine the product’s popularity because it is considered to represent the market performance of the product (Mayzlin & Chevalier, 2006). The number of reviews can also provide a reference to strengthen online shoppers’ confidence while reducing uncomfortable feelings of risk exposure (Buttle, 1998). In other words, consumers may perceive that more reviews represent a more popular product and greater importance. Therefore, we proposed the following hypothesis:
H2: A large quantity of online reviews will have a positive impact on purchasing intention.

The Elaboration Likelihood Model

Dual-process theories are often used to examine the role and influence of process in shaping consumer perception and behavior. Based on dual-process theories, attitude formation is not always based on effortful processing of persuasive information, but sometimes can be based on less effortful processing of heuristic cues. Dual-process theories further specify conditions under which each of the two processes is likely to occur (Chaiken & Trope, 1999). The elaboration likelihood model (ELM; Petty & Cacioppo, 1981, 1986) provides a useful framework for understanding the effectiveness of persuasive communication (Lee, 2009; Sher & Lee, 2009).

Based on the ELM, attitude change may occur via two routes of influence; the central route and the peripheral route. These two alternative routes differ in the amount of thoughtful processing of information or “elaboration.” Individuals taking the central route think critically about issue-related arguments and scrutinize the merits and relevance of those arguments before forming an attitude about an advertisement or product. On the other hand, individuals taking the peripheral route make less cognitive effort when forming attitude and rely on shortcuts such as the number of arguments and physical attractiveness of endorsers. In the ELM, it is posited that attitude change induced via the central route is more enduring and predictive of behavior than is attitude change induced by the peripheral route because the former route is based on deliberate and thoughtful consideration of relevant arguments (Petty & Cacioppo, 1986).

Petty and Wegener (1999) suggest individuals in a high elaboration likelihood state are more likely to engage in thoughtful processing of information and more likely to be persuaded by argument quality. On the other hand, people in a low elaboration likelihood state tend to base their attitude change on peripheral cues. Elaboration likelihood moderates the effects of argument quality and peripheral cues on attitude change. According to the ELM, elaboration likelihood is determined by an individual’s motivation and ability to elaborate. Motivation refers to the personal relevance to the individual of the persuasive message while ability is manifested in the individual’s prior expertise with the attitude object. Although individuals vary in their ability and motivation to elaborate, the moderating role of motivation was of interest in the present study. Need for cognition was adopted in the present study as a moderator and is reviewed in the ensuing section.

Need for Cognition

The relationship between personality variables and consumer behavior has been of great interest to researchers since the discipline of marketing was first established (Haugtvedt, Petty, & Cacioppo, 1992). Need for cognition, an important personality variable classified by Cacioppo and Petty (1982), refers to a stable individual difference in tendency to engage in, and enjoy, effortful cognitive activity, although this trait may be influenced by certain situational factors (Cacioppo, Petty, Feinstein, & Jarvis, 1996). The individual differences in need for cognition fall along a bipolar continuum anchored at low and high on both ends (Tuten & Bosnjak, 2001). Individuals with low need for cognition do not enjoy cognitive effort and prefer to rely on others’ (preferably experts’) opinions when dealing with complicated issues. They also tend to base their attitudes on simple cues such as the source attraction (Petty, Cacioppo, & Goldman, 1981) and the number of arguments offered by the messages (Petty & Cacioppo, 1984).

Those high in need for cognition have more cognitive resources available and are more likely to use systematic rules to process information. These individuals are described as highly intrinsic, motivated, and curious (Olson, Camp, & Fuller, 1984). Therefore, they are naturally motivated to seek and acquire information. The effects of need for cognition have been tested in various situations, such as information recall, cognitive and knowledge tests, text comprehension, and Internet use (Amichai-Hamburger et al., 2007; Cacioppo et al., 1996; Kaynar & Amichai-Hamburger, 2008). As an emerging information distribution tool, online review has become an important source of information for online shoppers. Online consumers vary in their tendency to engage in, and enjoy, information handling; however, there has been little research on need for cognition in online consumer behavior.

In the ELM it is suggested that whether the attitudes of individuals are based on the central or peripheral route depends on the level of need for cognition (Haugtvedt et al., 1992). Individuals who are motivated to process a message tend to base their judgment of persuasive arguments on the evaluation of message quality. They are more likely to engage in thoughtful thinking and attend to the quality of persuasive arguments. A high-quality online review is more logical and evaluation will be supported with facts and evidence. Therefore, the following hypothesis was proposed:
H3: The purchasing intention of individuals with a high need for cognition will be influenced more by argument quality than by quantity of online reviews.

The number of online reviews about a product implies the popularity of the product because the number of reviews may represent the number of the interested individuals with prior purchasing or usage experience (Chatterjee, 2001). In light of the ELM perspective, individuals with a low need for cognition expend less cognitive effort when processing persuasive information. They tend to base their attitudes on peripheral cues, such as the attractiveness of the source and number of arguments. Therefore, the following hypothesis was formulated:
H4: The purchasing intention of individuals with a low need for cognition will be influenced more by argument quantity than by quality of online reviews.

Method

Participants and Design

A total of 263 undergraduate students participated in the online experiment for extra credit. The students were randomly assigned according to a factorial design. Argument quality was manipulated at two levels: high or low, while quantity was manipulated at two levels: large or small. The students participated in groups of 10 to 15 in a large computer laboratory. They were separated from each other and were asked to complete the experiment independently.

Procedure

A virtual shopping mall website – the X Shopping Mall – was created for the study. A focus group of 20 students with online shopping experience who did not participate in the later experiment took part in a preliminary meeting to decide for which product categories consumers most often choose to shop online. The cell phone was selected as the focal product category on the basis of majority opinion. This result was consistent with the classification by Girard, Silverblatt, and Korgaonkar (2002) of the cell phone as a search product. Consistent with the findings of Park and colleagues (2007), the focus group agreed that the average number of online reviews which they perceived as a large quantity was 6, and a perceived small quantity was 1. In order to determine the argument quality (AQ) of online reviews, 30 online reviews about cell phone products were selected from Amazon.com. These reviews were pretested for argument quality by 30 students who were not taking any part in the experiment. High argument quality was defined as understandable, objective, and supported with relevant facts. Based on majority opinion, the pretest yielded 6 strong-argument reviews and 6 weak-argument reviews.

When they had entered the computer laboratory, each participant in the online experiment sat down in front of a desktop computer, to which a website corresponding to one of the four conditions had been randomly assigned. Participants were asked to read the instructions on the computer screen. On the first page of X Shopping Mall the purpose of study was introduced. The participants were told that a new type of cell phone that had already been introduced in another market was going to be introduced soon in the local market. The purpose in the study was to assess its market viability. The second page of the experimental website displayed seller-generated information advertising the “A Model” cell phone. This consisted of a color photo and a list of five product features. After viewing the product information, the following pages contained the content and statistics of online reviews. Online reviews varied for each participant in terms of quality and quantity. On average, each student took 15 minutes to complete the online experiment. Participants were debriefed about the nature of study before leaving the computer laboratory and asked not to discuss it with others.

Measures

The students were instructed to respond to measures of manipulation checks before taking dependent measures – purchasing intention – and demographic questions. The manipulation check for the quality of reviews consisted of three items rated on 7-point scales (1 = disagree strongly to 7 = agree strongly): (1) Do these online reviews present sound arguments? (2) Are these online reviews credible? And (3) Do these online reviews provide facts in support of their position? (Park et al., 2007) The manipulation check on the quantity of reviews measured participants’ perceptions of the quantity of reviews. On the experimental website, the number of reviews for the focal product was juxtaposed with the numbers of reviews for other cell phones that were not part of the experiment. The number for the focal product was chosen deliberately to contrast with the number for the cellphones that were not part of the experiment. The students were then asked to recall the number of reviews and indicate whether it was more or less than the number of reviews for other cell phones. The dependent measure of participants’ purchasing intention contained two questions: (1) How likely is it that you would choose the “A Model” cell phone next time you plan to purchase a cell phone with similar features? And (2) Would you recommend the “A Model” cell phone to your friends? These two items were also anchored on a 7-point Likert scale. The students were then asked to rate the 18-item need for cognition measure developed by Petty and Cacioppo (1984) on a scale from 1 to 7 where 1 = disagree strongly and 7 = agree strongly. Half of the need for cognition items were reverse scored. Finally, the students were asked to provide demographic information.

Results

Manipulation Checks

The internal consistency of items in the manipulation check for argument quality of online reviews was found to be α = 0.78. These items were further averaged for an analysis of variance (ANOVA). The result provided evidence that the argument quality was successfully manipulated [F(1, 261) = 11.972, p < 0.001, M = 3.88, and 4.36]. In addition, the percentage of students who recalled the number of reviews correctly was 93%, while 80% of students gave the correct answer when asked whether the number was larger or smaller than for the other cell phone reviews. Therefore, the quantity of online reviews was also judged to be manipulated successfully (Petty et al., 1983).

Hypothesis Testing

Participants were categorized as high or low in need for cognition via a median split (Mdn = 72; M low need for cognition = 66.1, M high need for cognition = 79.5). In Table 1, the descriptive statistics of purchasing intention scores are summarized. The data underwent a 3-factor ANOVA with results showing a significant main effect of argument quality [F(1, 255) = 7.498, p < 0.05] and a significant main effect of review quantity [F(1, 255) = 4.025, p < 0.05]. In other words, online reviews of a high quality had a positive impact on the purchasing intention of online shoppers and when there were many online reviews this had a positive impact on the purchasing intention of online consumers. Therefore, Hypotheses 1 and 2 were supported.

As predicted in the ELM, a significant effect of argument quality x need for cognition interaction [F(1, 255) = 6.113, p < 0.05] emerged from the ANOVA. Specifically, the main effect of argument quality manipulation was qualified by an argument x need for cognition interaction. The argument quality x need for cognition interaction suggested that participants with a high need for cognition took the central route in formulating purchasing intention. Simple main effects tests were conducted to explore the effect further. The results revealed participants with a high need for cognition expressed more positive attitudes after exposure to the strong argument quality version (M = 4.54) than after exposure to the weak argument quality version (M = 3.82) of online reviews [F(1, 137) = 12.652, p < 0.001], but that attitudes of the participants with a low need for cognition exposed to strong (M = 3.99) and weak (M = 4.01) argument quality reviews did not differ.

On the other hand, a significant quantity x need for cognition interaction emerged from the 3-factor analysis [F(1, 255) = 5.430, p < 0.05]. Specifically, the main effect of review quantity manipulation was qualified by a quantity x need for cognition interaction. This finding was consistent with the prediction according to the ELM. In other words, the quantity x need for cognition interaction indicated that the students with a low need for cognition took the peripheral route in formulating purchasing intention. Simple main effects tests were conducted to explore the effect further. The results revealed that low need for cognition students expressed more positive attitudes after exposure to the large quantity version (M = 4.30) than after exposure to the small quantity version (M = 3.71) of online reviews [F(1, 122) = 11.374, p < 0.001], but that attitudes of high need for cognition students did not differ when they were exposed to many (M = 4.22) or few (M = 4.17) reviews.

In summary, in the present study the purchasing intention of online shoppers with a high need for cognition was influenced more by argument quality than by quantity of online reviews. In addition, the purchasing intention of online consumers with a low need for cognition was influenced more by argument quantity than by quality of online reviews. Thus, Hypotheses 3 and 4 were supported.

Discussion

Online review is one of the most important communication channels. Researchers have provided evidence in support of its effects on sales (Mayzlin & Chevalier, 2006). As a result, management of online reviews has increasingly been integrated into marketing communication strategy. The purposes in the present study were to explore the effects of positive online reviews on the attitudes of online consumers and the mechanisms supporting these effects. Four major findings emerged from results. Firstly, online reviews of a high quality had a positive effect on the purchasing intention of online shoppers. Reviews with strong argument quality supported with facts are perceived as being more objective. Consequently, they are more persuasive than weak quality argument reviews that are subjective and emotional.

Secondly, having a high number of reviews positively affects the purchasing intention of online consumers. Specifically, the attitudes of online consumers become more positive as the number of reviews increases. When there is a large number of reviews this is perceived as an indication of product popularity and hence the purchasing intention of consumers increases. In addition, the main effect of argument quality manipulation is qualified by an argument x need for cognition interaction. People with a high need for cognition take the central route in attitude change when shopping online; that is, they are influenced more by review quality. This finding contributes to the ELM research literature by extending the application of ELM to online consumer behavior. From the central route perspective, marketing managers and online review software developers should carefully consider the right match between the level of consumer elaboration likelihood and the digital presentation of review contents. For example, online consumers wishing to post their reviews for a high need for cognition audience may be directed to present their opinions in a certain designated format to ensure the review quality. Finally, consistent with the ELM theory, in the current study the main effect of review quantity manipulation was qualified by a quantity x need for cognition interaction. Low need for cognition consumers tended to adopt the peripheral route in forming attitudes in an online context; that is, they were persuaded more by online review quantity because more reviews are perceived as indicating greater product popularity. This finding contributes to a better understanding of the mechanism underpinning the effect of online reviews. For marketing executives and software developers, the peripheral route perspective suggests the importance of generating as many reviews as possible for a low need for cognition audience. For example, ensuring that there is a user-friendly interface may simplify the process of posting reviews and thereby increase the participating intention. Incentives such as a prize draw and online games can also enhance online shoppers’ willingness to post reviews.

Understanding the effects of personality factors on the formation of consumer attitudes provides a number of benefits for marketing executives. For example, knowledge of the level of need for cognition can guide Internet marketers and software developers to design appropriate promotional materials and review formats in order to influence online shoppers effectively. Further, demographic profiles of people with various personality attributes (such as high and low need for cognition) are essential for segmenting and targeting the market. By using need for cognition as a moderator in the present study, more knowledge has been gained about the new marketing tool of online reviews, and a contribution has been made to the ELM literature by testing the model in an online context. The results indicate that the behaviors of Internet users can be explained by the same model originally developed to explore real-world behaviors. We believe that the ELM approach gives researchers a useful way to better understand the mechanisms by which online consumers develop and maintain attitudes. Haugtvedt et al. (1992) suggested that combining extrinsic factors (such as situational involvement and mood) and intrinsic factors (such as need for cognition and skepticism), will allow researchers to identify maximal differences in information processing and, over time, to predict consumer attitudes more accurately. While personality traits may be influenced by certain situational factors (Cacioppo et al., 1996), the joint effects of situational factors and dispositional factors are yet to be explored by researchers interested in the effects of online reviews and the mechanism underpinning these effects.

References

Amichai-Hamburger, Y., Kaynar, O., & Fine, A. (2007). The effects of need for cognition on Internet use. Computers in Human Behavior, 23(1), 880-891.

Allsop, D. T., Bassett, B. R., & Hoskins, J. A. (2007). Word-of-mouth research: Principles and applications. Journal of Advertising Research, 47, 398-411.

Buttle, F. A. (1998). Word of mouth: Understanding and managing referral marketing. Journal of Strategic Marketing, 6(3), 241-254.

Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116-131.

Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, W. B. G. (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in need for cognition. Psychological Bulletin, 119(2), 197-253.

Chaiken, S., & Trope, Y. (1999). Dual-process theories in social psychology. New York: Guilford.

Chatterjee, P. (2001). Online reviews: Do consumers use them? Advances in Consumer Research, 28, 129-133.

Girard, T., Silverblatt, R., & Korgaonkar, P. (2002). Influence of product class on preference for shopping on the Internet. Journal of Computer-Mediated Communication, 8, 101-120.

Haugtvedt, C. P., Petty, R. E., & Cacioppo, J. T. (1992). Need for cognition and advertising:

Understanding the role of personality variables in consumer behavior. Journal of Consumer Psychology, 1, 239-260.

Kaynar, O., & Amichai-Hamburger, Y. (2008). The effects of cognition on Internet use revisited. Computers in Human Behavior, 24(2), 361-371.

Keller, E. (2007). Unleashing the power of word of mouth: Creating brand advocacy to drive growth. Journal of Advertising Research, 47(4), 448-452.

Lee, S. H. (2009). How do online reviews affect purchasing intention? African Journal of Business Management, 3, 576-581.

Mayzlin, D., & Chevalier, J. A. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345-354.

Olson, K., Camp, C., & Fuller, D. (1984). Curiosity and need for cognition. Psychological Reports, 54(1), 71-74.

Park, D-H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125-148.

Petty, R. E., & Cacioppo, J. T. (1981). Attitudes and persuasion: Classic and contemporary approaches. Dubuque, IA: Brown.

Petty, R. E., & Cacioppo, J. T. (1984). The effects of involvement on response to argument quantity and quality: Central and peripheral routes to persuasion. Journal of Personality and Social Psychology, 46(1), 69-81.

Petty, R. E., & Cacioppo, J. T. (1986). Communication and persuasion: Central and peripheral routes to attitude change. New York: Springer-Verlag.

Petty, R. E., Cacioppo, J. T., & Goldman, R. (1981). Personal involvement as a determinant of argument-based persuasion. Journal of Personality and Social Psychology, 41(5), 847-855.

Petty, R. E., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10, 135- 146.

Petty, R. E., & Wegener, D. T. (1999). The elaboration likelihood model: Current status and controversies. In S. Chaiken & Y. Trope (Eds.), Dual-process theories in social psychology. New York: Guilford.

Sher, P. J., & Lee, S. H. (2009). Consumer skepticism and online reviews: An elaboration likelihood model perspective. Social Behavior and Personality: An international journal, 37(1), 137-144.

Tedeschi, B. (1999, October 25). Consumer products and firms are being reviewed on more web sites, some featuring comments from anyone with an opinion. New York Times, Section B, p. 1.

Tuten, T., & Bosnjak, M. (2001). Understanding differences in web usage: The role of need for cognition and the five factor model of personality. Social Behavior and Personality: An international journal, 29(4), 391-398.

Amichai-Hamburger, Y., Kaynar, O., & Fine, A. (2007). The effects of need for cognition on Internet use. Computers in Human Behavior, 23(1), 880-891.

Allsop, D. T., Bassett, B. R., & Hoskins, J. A. (2007). Word-of-mouth research: Principles and applications. Journal of Advertising Research, 47, 398-411.

Buttle, F. A. (1998). Word of mouth: Understanding and managing referral marketing. Journal of Strategic Marketing, 6(3), 241-254.

Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116-131.

Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, W. B. G. (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in need for cognition. Psychological Bulletin, 119(2), 197-253.

Chaiken, S., & Trope, Y. (1999). Dual-process theories in social psychology. New York: Guilford.

Chatterjee, P. (2001). Online reviews: Do consumers use them? Advances in Consumer Research, 28, 129-133.

Girard, T., Silverblatt, R., & Korgaonkar, P. (2002). Influence of product class on preference for shopping on the Internet. Journal of Computer-Mediated Communication, 8, 101-120.

Haugtvedt, C. P., Petty, R. E., & Cacioppo, J. T. (1992). Need for cognition and advertising:

Understanding the role of personality variables in consumer behavior. Journal of Consumer Psychology, 1, 239-260.

Kaynar, O., & Amichai-Hamburger, Y. (2008). The effects of cognition on Internet use revisited. Computers in Human Behavior, 24(2), 361-371.

Keller, E. (2007). Unleashing the power of word of mouth: Creating brand advocacy to drive growth. Journal of Advertising Research, 47(4), 448-452.

Lee, S. H. (2009). How do online reviews affect purchasing intention? African Journal of Business Management, 3, 576-581.

Mayzlin, D., & Chevalier, J. A. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345-354.

Olson, K., Camp, C., & Fuller, D. (1984). Curiosity and need for cognition. Psychological Reports, 54(1), 71-74.

Park, D-H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125-148.

Petty, R. E., & Cacioppo, J. T. (1981). Attitudes and persuasion: Classic and contemporary approaches. Dubuque, IA: Brown.

Petty, R. E., & Cacioppo, J. T. (1984). The effects of involvement on response to argument quantity and quality: Central and peripheral routes to persuasion. Journal of Personality and Social Psychology, 46(1), 69-81.

Petty, R. E., & Cacioppo, J. T. (1986). Communication and persuasion: Central and peripheral routes to attitude change. New York: Springer-Verlag.

Petty, R. E., Cacioppo, J. T., & Goldman, R. (1981). Personal involvement as a determinant of argument-based persuasion. Journal of Personality and Social Psychology, 41(5), 847-855.

Petty, R. E., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10, 135- 146.

Petty, R. E., & Wegener, D. T. (1999). The elaboration likelihood model: Current status and controversies. In S. Chaiken & Y. Trope (Eds.), Dual-process theories in social psychology. New York: Guilford.

Sher, P. J., & Lee, S. H. (2009). Consumer skepticism and online reviews: An elaboration likelihood model perspective. Social Behavior and Personality: An international journal, 37(1), 137-144.

Tedeschi, B. (1999, October 25). Consumer products and firms are being reviewed on more web sites, some featuring comments from anyone with an opinion. New York Times, Section B, p. 1.

Tuten, T., & Bosnjak, M. (2001). Understanding differences in web usage: The role of need for cognition and the five factor model of personality. Social Behavior and Personality: An international journal, 29(4), 391-398.

Appreciation is due to reviewers including

Danny Wang

National Taipei University of Technology

Taipei

Taiwan

ROC

Email

[email protected]

Shen-Hsien Lee, Department of Business Administration, College of Management, Yu-Da University, No.168, Hsueh-Fu Rd., Miao-Li County, 36143, Taiwan, ROC. Phone: +886-935-596518; Fax: +886-37-651216; Email: [email protected]