Effects of Negative Online Reviews on Consumers’ Attitudes and Behavioral Intentions toward Online P

Economic Management Journal November 2015, Volume 4, Issue 4, PP.75-83

Effects of Negative Online Reviews on Consumers’ Attitudes and Behavioral Intentions toward Online Products* Yuhua Cui†, Jishun Niu, Rui Guo School of Business, Beijing Institute of Fashion Technology, 100020, China †

Corresponding Author Email: cuiyuhua@snu.ac.kr, corresponding author

Abstract Online shopping has become an important new channel because of its rapid development and broad application of the Internet technology. As consumer information search and release gradually shift from offline to online, online reviews of products have become more valuable. Research shows that most online shoppers view online reviews from product users before purchasing. As one of the most important forms of spreading awareness, online product reviews has an increasing impact on customer purchase decisions and has gradually become an urgent issue in network marketing research. This phenomenon impels businesses to realize that online reviews significantly affect trading volume. Businesses have attempted to manipulate online reviews by providing a significant number of positive comments that could lead to consumer confidence and purchase of products. Internet users in China are more interested in reading negative comments compared with the rest of the global Internet users. Thus, the effects of structural characteristics of potential customers, negative attitudes, and behavioral intentions have not yet aroused global concern because this phenomenon has been limited to the local scale.

Based on literature, the main objective of negative online reviews is the positioning of fashion products. The perception of negative online reviews, purchase attitudes, and behaviors are the factors considered in the present study. Negative online reviews of clothing and accessories sold online and their influence on consumer purchase intention and attitudes are analyzed. The study aims to confirm that negative online reviews have an effect on consumer purchase intention, attitudes, and behaviors. Moreover, buying attitudes influence behavior intention.

Keywords: Online reviews; Purchase attitudes; Behavioral intention

1 INTRODUCTION The emergence of online shopping has made lives convenient and fast. According to the Number of Internet Users (2015), nearly 75% of all internet users in the world (2.8 billion) live in the top 20 countries. China, the country with most users (642 million in 2014), represents nearly 22% of total. Merely shopping online in China has become popular. Shopping online has become an integral part of many people’s daily lifestyle due to features such as instant messaging, product personalization, integration, and popular product suggestions. The basic concept of online shopping is to make the traditional store accessible from home, allowing consumers to purchase and enjoy goods and *

Foundation 1: Science and Technology Development Project of Beijing Education Commission, 2015 (Study on Consuming Behavior Data Mining and Application Key Technology under Fashion Electronic Commerce). Project Number: KYJH02150201/021/001 Foundation 2: Reform in Education Team Building Project of Beijing Institute of Fashion Technology, 2014 (Electronic Commerce Data Analyst Courses Team Building). Project Number: JXTD-1403 Foundation 3: The Project of Construction of Innovative Teams and Selection and Development of Excellent Talents for Beijing Institute of Fashion Technology (PTTBIFT) - 75 www.emj-journal.org

services in a more convenient manner.

Customers check reviews when shopping online (Econsultancy, 2014). Purchase reviews are used by customers as an important basis for their purchase decisions. The process of online shopping is simple and incorporates consumer demand, commodity information collection, contrast selection, purchasing, and behaviors after purchase. Online reviews have also become an important basis for product comparison.

Surveys and research data on online consumers have shown that an increasing number of users utilize search engines to understand product information before purchasing (Econsultancy, 2014). They inquire about many aspects of demand: brand, brand reputation, and comparison of goods at different price points. Online reviews offer the most direct answers because they show the true feelings of customers and allow consumers to learn about the advantages, shortcomings, best uses, cost effectiveness, user experience, and purchase considerations of goods.

A number of studies on the positive online reviews from China and abroad exist (Duan et al., 2008; Econsultancy, 2014). However, only a few studies have been conducted on negative reviews. Negative comments have an unusual value for customers and website or business owners. Goods that have generated negative information belong to the expected consumption risk in consumer psychology. Rational consumers are more concerned about the negative information of products than emotional consumers. Thus, negative information on goods plays a referencing role in the purchase attitudes. Consumers understand the shortcomings of goods from negative reviews and develop psychological expectations. Consumer satisfaction derived from knowing these shortcomings prior to purchase and use is much higher than that of knowing these shortcomings after use. Consumers predict negative information of products but become increasingly satisfied after the purchase. Consumer and business websites are beneficial; however, negative reviews may reduce consumer confidence on other commodities in the same store and affect consumer purchase attitudes, reducing the desire to buy. Consumer and business sites are not beneficial because negative online reviews have advantages and disadvantages. For consumers, the number of excessive praise or absence of negative feedback gives consumers a perceived risk (Bansal & Voyer, 2000). Negative reviews have higher credibility and are more general compared with positive reviews. Therefore, consumers are often more sensitive and more likely to accept negative reviews (Mizerski, 1982).

This study mainly focuses on the relationship of negative online reviews, purchase attitudes, and behavioral intentions. The main methods of regression analysis and literature research are complemented by questionnaires to investigate the effect of negative online reviews on customer purchase attitudes and behaviors.

2 LITERATURE REVIEW 2.1 Negative online review studies 1) Online Reviews Online consumer reviews are a major form of a word-of-mouth (WOM) networking. Consumers buy, use, and evaluate products or services on the network, with respect to the information released by their respective companies.

Online consumer reviews reflect more real and comprehensive information (Chevalier & Mayzlin, 2006).

WOM communication is any positive or negative statement made by potential, actual, or former customers about a product or company. The information is made available to a multitude of people and institutions via the Internet (Hennig-Thurau, 2004). WOM includes real and trustworthy information on product experience, price, and performance and other statements or reviews that consumers can collect through a network platform and react to (Hennig-Thurau, & Walsh, 2003).

Although some definitions about online reviews are different (Chevalier & Mayzlin, 2006; Hennig-Thurau, 2004), the following points show similarities. (1) Online reviews are the most efficient means of communication. This type is different from ordinary WOM and has opened a new direction. (2) Online reviews offer authenticity, convenience and efficiency.

2) Negative Online Reviews - 76 www.emj-journal.org

Consumers in a traditional environment can share their dissatisfaction to five people, whereas consumers in a network environment can disseminate this information to 6,000 or even more (Hanson, 2000). Richins (1983) stated that negative WOM means that consumers who are not satisfied with products will pass to other consumers their emotional experiences. Liang and Chen (2006) argued that the negative comments are based on customers’ own dissatisfaction with products and services and these customers shares their experiences with friends and relatives, advising them not to buy the products or services.

Thus, informed, negative reviews result to significantly low consumer satisfaction. Based on previous studies (Chen, 2006; Richins, 1983), negative online commenting is defined as an Internet platform for dissatisfied consumers to share negative information and feelings toward a product or service.

3) Effect of Negative Online Reviews Network development worldwide aids in the rapid growth of online negative reviews and in the emergence of several interesting phenomena, including the preference of Chinese Internet users for negative reviews. In 2010, the research company Nielsen (2010) released a report on the habits of Internet users from Asia-Pacific countries. The report stated that among the citizens of the entire Asia-Pacific region, the Chinese Internet users have a higher preference for releasing negative product reviews, and that in terms of willingness, the Chinese Internet users are the only groups that are more willing to post negative reviews than positive comments. Moreover, approximately 62 % of China’s Internet users expressed more willingness to share negative reviews. Among the total number of the world's Internet users, 41% composed of Chinese Internet users suffer from the so-called "bad news syndrome" (Nielsen, 2010).

Chevalier and Mayzlin (2006) examined the effect of consumer reviews on relative sales of books on Amazon.com and Barnes and Noble.com. They found that the effect of 1-star reviews is greater than the impact of 5-star reviews.

In the marketing field, Ahluwalia et al. (2000) found that consumers often think that negative information have more value than positive information and thus, they would rely more on the negative information in their purchase decisions. This development reflects the awareness of rights of Internet users in the online shopping process enhancements. Negative reviews and lesser customer purchases in shops are evidences that more and more people are concerned about and believe in negative reviews.

2.2 Attitudes and Behavioral Intentions toward Online Products 1) Purchase Attitudes Attitude is defined as consumers’ overall evaluation of a product whether good or bad (Low & Lamb, 2000).

Attitude individually holds some stable psychological tendencies on specific objects (people, ideas, emotions, or events). Attitude has self-protection features for consumer protection and understanding of authenticity and quality of objects. Attitude is not only an appraisal but also a psychological tendency. Attitude is produced from the perception of information. The present paper presents purchase attitudes stemming from the perception of negative reviews for consumers.

2) Behavioral Intentions Behavioral intention is one of a very small set of variables that find routine application in consumer research investigations undertaken for a variety of different purposes and covering a broad range of products and services (Kalwani & Silk, 1982). Behavioral intention denotes various actions of consumers to obtain, use, and dispose consumer goods or services, as well as decisions on these actions and decision-making process. Behavioral intention is closely linked to the exchange of goods or services. Consumers make choices through evaluating goods to form purchase intention. Consumers often buy their favorite brands under normal circumstances. However, they are sometimes affected by the attitudes of others and accidents, which change their purchase decisions.

3 METHOD 3.1 Research Framework - 77 www.emj-journal.org

This study was implemented based on three perspectives. First, the negative reviews, which are perceived as independent variables of purchase attitudes and behavioral intentions, were used as dependent variables to study the perception of negative review influence on consumer purchase attitudes and behavior intentions. Second, the purchase attitude and behavioral intentions were used as independent variable and dependent variable, respectively, to study the influence of consumer’s purchase attitudes on behavior intention. The model is shown in Figure 1.

Purchase attitudes

H1 Perception of negative online reviews

H2

H3

Behavioral intentions

FIG. 1 RESEARCH MODEL

1) Influence of Negative Reviews on Purchase Attitudes Katz and Lazarsfeld (1970) showed that consumer attitudes within the negative to the positive process review the role of advertising approximately nine times. Online negative reviews have a reference influence on consumer purchase attitudes. Attitude functional theory holds that people carry a certain attitude to meet their internal needs.

Moreover, changing people's attitudes is easier if outside information meets their demand (Song et al., 2009). Highvalue negative reviews provide consumers more reliable information and are entirely likely to affect attitudes toward purchase.

Hypothesis 1: Negative reviews have a significant negative influence on consumer purchase attitudes.

2) Influence of Purchase Attitudes on Behavioral Intentions Chen (2009) demonstrated that consumers’ positive or negative attitudes from a network positively or negatively affect their willingness to buy. A higher purchase intention corresponds to a higher purchase probability (Lapierre, 2000). Driver et al. (1991) showed that positive or negative attitudes have an important influence on behavior willingness, that is, positive attitudes bring higher willingness, and negative attitudes bring low behavioral intent.

Consumer behavior intentions show the consistency of attitudes.

Hypothesis 2: Purchase attitudes have a significantly positive influence on consumer behavior intentions.

3) Influence of Negative Reviews on Behavioral Intentions Senecal and Nantel (2004) stipulated that consumers who consult product recommendations are more likely to select the recommended product than consumers who do not consult a recommendation source. The determining factor of online consumers’ purchase intentions and adoption is largely based on theoretical, subjective, and perceived behavioral controls.

Hypothesis 3: Negative reviews have a significantly negative influence on consumer behavior intentions.

3.2 Measures Measurement questions were designed through three variables. The questionnaire mainly measured collected reviews of online apparel stores.

TABLE 1. MEASUREMENTS Variable The perception of negative reviews Purchase attitudes Behavioral intentions

Question There are too many negative reviews on this online store.

Negative reviews are worth trusting.

Negative reviews are worth watching.

Products in this online store will be good.

Products in this online store have good quality.

Products in this online store will be valuable.

I will buy the products in this online store.

I will recommend the products to others.

- 78 www.emj-journal.org

Origin of questions Guo (2012) Low & Lamb (2000) Kalwani & Silk (1982)

3.3 Data Collection This study used the empirical method to study the effect of negative reviews on attitudes and behavioral intentions of consumers. The questionnaire has two parts. The first part is about the recent exposure of the respondents to cases of negative reviews on online apparel product information. The questions of this study are related to the measurement of the three variables. The second part is the basic personal information of the respondents, including five questions on gender, age, education, occupation, and income. In addition to the basic personal information, and general use options, other question items used the 5-point Likert subscale method to measure the feelings toward each index item based on their score for each question: 1, 2, 3, 4 and 5 stand for completely disagree, comparatively disagree, general, inclined to agree, and completely agree, respectively.

This study focused on the impact of negative reviews on consumer purchase attitudes and behavioral intentions.

Therefore, the criteria for research object selection are multiple online shopping experiences and access to product or service information channels on the Internet. A survey questionnaire company (questionnaire net http://www.wenjuan.com) was provided to respondents who have met the criteria for 30 days starting March 10, 2015. A total of 331 accomplished questionnaires were collected.

4 RESULTS The research model was tested with data collected from Chinse consumers located in mainland China. A total of 331 questionnaires were distributed. All accomplished questionnaires were valid, and the data were subjects to variable effect analysis and model hypothesis testing. Descriptive statistical analysis and reliability and validity analysis used data collected by SPSS 20.0 to achieve satisfactory results.

4.1 Samples This study collected 331 valid questionnaires, 105 were from men (31.7%), and 226 were from women (68.3%).

Among the respondents, 83.1% belong to the 20–29 age group, whereas 6.0% belong to the 30–39 age group. The respondents were mainly young people because they are deemed as more familiar with the network environment and more able to provide research data. Respondents with high school education or less composed 9.5% and university or college graduates composed 81.9% of the total. Students composed 62% of the respondents. Unmarried and married respondent composed 84% and 16% of the total, respectively. More than 60% of the respondents were earning a monthly income of one 5000 RMB or less.

4.2 Reliability and Validity Analysis Before proceeding with hypothesis testing, the study investigated reliability and validity assessments. The Cronbach’s alphas of this study proved the reliability of the measurement items (Carmines & Zeller, 1979). The responses to three questions relating to negative reviews perception were combined to produce the perception of negative reviews (Cronbach’s Alpha = .90), also purchase attitudes (Cronbach’s Alpha = .92), behavioral intention (Cronbach’s Alpha = .89). Exploratory factor analysis was also conducted to verify convergent validity and discriminative validity. Principal component analysis was conducted with varimax rotation, and an eigenvalue of 1 was used as the selection criterion. Items with a loading value of 0.6 or lower were disregarded during the process of reviewing the results of the exploratory factor analysis.

4.3 Correlation Analysis Correlation analysis is a method of determining a variable or multiple variables between two closely interrelated statistics extents. The correlation coefficient r indicates the closeness of the relationship between variables. In the range of the correlation coefficient r of [−1, 1], an r closer to −1 or +1 corresponds to a closer relationship between variables, whereas an r closer to 0 shows that the relationship between variables is not close. Pearson correlation analysis was used to analyze perception of negative reviews, purchase attitudes, and behavioral intentions, to detect the degree of correlation between variables. The correlation analysis results by SPSS 20.0 are presented in Table 2.

- 79 www.emj-journal.org

TABLE 2. CORRELATION ANALYSIS RESULTS FOR EACH VARIABLE Perception of Negative reviews

Purchase attitude

Perception of Negative reviews 1 Purchase attitude −0.291** 1 Behavioral intention −0.290** 0.663** NOTE: **significant correlation on the 0.01 level (both sides)

Behavioral intention

1

The correlation analysis results show that the perception of negative reviews, purchase attitudes, and behavioral intentions with a significant level of 0.01 is negatively correlated with higher negative reviews perception, consumer attitudes, and lower behavioral purchase intentions. Lower perception negative reviews and consumer buying attitudes correspond to higher behavioral intentions. Purchase attitudes toward behavioral intentions show a positive correlation at 0.01 level of significance. A higher purchase attitude corresponds to a higher behavioral intention, whereas a lower consumer purchase attitude corresponds to a lower behavior intention. The purchase attitudes toward behavioral intentions of Pearson coefficient reached a maximum of 0.663, when 1, 2, and 3 were assumed as preliminary verifications.

4.4 Regression Analysis The correlation analysis found that the perception of negative reviews has a significant negative correlation with purchase attitudes and behavioral intentions at the level of 0.01. Purchase attitudes toward behavioral intentions were positively correlated at 0.01 significance level. This section discusses the regression analysis variables presented above.

1) The effect of perception of negative reviews on purchase attitudes The results of the regression analysis are shown in Table 3. These results were derived by using the perception of negative reviews as the independent variable and purchase attitude as the dependent variable.

TABLE 3. REGRESSION ANALYSIS OF PERCEPTION OF NEGATIVE REVIEWS AND PURCHASE ATTITUDES Dependent variable

Independent variable

Adjusted R2

F value

Standardized coefficient

T value

Sig.

Purchase attitude

Perception of Negative reviews

0.082

30.381***

−0.291***

−5.512

0.000

NOTE: ***p <0.001 (two-tailed)

The model shows that the adjusted coefficient R2 is 0.082, indicating that this regression equation could explain 8.2% of the total variance and the F value of 30.381. Perceived significance probability of negative reviews is less than 0.05; the regression results of the purchase attitude are obvious; the regression coefficient is negative. These indicate that the negative reviews have a significantly negative impact on consumer purchase attitude; the standardized regression coefficient is −0.291. Thus, Hypothesis 1 was supported.

2) Regression analysis for the Purchase attitudes to Behavioral intentions.

The results of the regression analysis are shown in Table 4. These results were derived by using purchase attitudes as the independent variable and behavioral intentions as the dependent variable.

TABLE 4. REGRESSION ANALYSIS OF PURCHASE ATTITUDES AND BEHAVIORAL INTENTIONS Dependent variable

Independent variable

Adjusted R2

F value

Standardized coefficient

T value

Sig.

Behavioral intention

Purchase attitude

0.438

257.906***

0.663***

16.059

0.000

NOTE: ***p <0.001 (two-tailed)

The adjusted coefficient of determination R2 is 0.438, indicating that this regression equation could explain 43.8% of the total variance. An F value of 257.906 reached a significant level, indicating good results. The significance probability of purchase attitude is less than 0.05; the intention to conduct regression results is significant; the - 80 www.emj-journal.org

regression coefficient is positive. These indicate that a positive purchase attitude has a significant effect on consumer behavioral intentions. The standardized regression coefficient is 0.663. Thus, Hypothesis 2 was supported.

3) Regression analysis for perception of negative reviews to behavioral intentions.

The results of the regression analysis are shown in Table 5. These results were derived by using perception of negative reviews as the independent variable, and behavioral intentions as the dependent variable.

TABLE 5 REGRESSION ANALYSIS OF PERCEPTION OF NEGATIVE REVIEWS AND BEHAVIORAL INTENTIONS Dependent variable

Independent variable

Adjusted R2

F value

Standardized coefficient

T value

Sig.

Behavioral intention

Perception of Negative reviews

0.082

30.292***

â&#x2C6;&#x2019;0.290***

â&#x2C6;&#x2019;5.504

0.000

NOTE: ***p <0.001 (two-tailed)

The model determines the adjusted coefficient R2 of 0.082, indicating that this regression equation could explain 8.2% of the total variance, and the F value of 30.292. The perception of the significance probability of negative reviews is less than 0.05. The effect on behavioral intention is significant. The regression coefficient is negative. These findings indicate that negative reviews exert a significant negative effect on consumer behavioral intentions. The standardized regression coefficient is â&#x2C6;&#x2019;0.290. Thus, Hypothesis 3 was supported.

5 CONCLUSION The design model framework of this article used the negative reviews perception and purchase attitudes as the independent variables. The following conclusions are drawn.

(1) The perception of negative reviews has a negative significant influence on the purchase attitudes of consumers. A higher perception of negative reviews corresponds to a lower consumer purchase attitude. This finding is consistent with those of Katz and Lazarsfeld (1970) and other scholars. Hypothesis 1 is verified. When the strength of consumer perception of the negative reviews has not reached a certain level, it is a contradiction of reference, and purchase attitudes will not be affected.

(2) The consumer purchase attitudes significantly affect their behavioral intentions. A higher purchase attitude corresponds to a higher consumer behavioral intention. This finding is consistent with those of Kalwani and Silk (1982) and other scholars. Hypothesis 2 is verified. Consumer behavior is a variety of actions (i.e., use and dispose of consumer goods or services). Mental attitude can be described as the tendency of consumers to buy goods or services. A more serious tendency leads to a higher possibility of consumer behavior.

(3) Negative reviews perception directly influences consumer behavioral intentions. A perception of negative reviews corresponds to lower consumer behavioral intentions. This finding is consistent with those of Senecal and Nantel (2004). Hypothesis 3 is verified. Chevalie and Mayzlin (2006), who studied the influence of online reviews of books sales, found that the evaluation of the influence of 1-star is higher than that of a 5-star. Negative reviews directly affect sales; consumers refuse to buy merchandise that has been negatively evaluated. Therefore, negative reviews are direct perceptions of the effect on consumer behavioral intention.

Negative reviews can be obtained by influencing consumer attitudes toward indirectly affecting their behavioral intentions and having a direct impact on consumer behavior.

5.1 Management Recommendations (1) The spread of negative online reviews should be given more attention. Consumer access to online negative comments weakens purchase attitudes and behavior as well as consumer trust. Before making purchase decisions, most Internet users visit online shops or go through online reviews to obtain product information. Visitation has become a habit, and has a strong influence on development. The spread of online reviews has a larger influence than traditional comments because the process is quicker. Consumers can immediately publish the negative information on different product review platforms and community forums, which can affect consumer buying decisions.

- 81 www.emj-journal.org

Therefore, as the Internet continues to gain popularity, on-line business enterprises should pay enough attention to negative comments.

(2) Consumer perception of negative reviews should be reduced. This study shows that the perception of negative reviews on consumer attitudes and behavioral intentions influence purchasing; therefore, reducing negative reviews can improve consumer purchase attitudes and behavioral intentions.

5.2 Limitations and Prospects The existing research results about negative online reviews are based on data collected in China and abroad. These results have produced some innovative research findings through empirical work on previous comprehensive and complementary studies. However, the capacity of the researcher of the present work and the conditions created limitations.

(1) Excessively concentrated samples The abovementioned descriptive statistical analysis results are more concentrated on respondents who are college and university graduates between the ages of 20â&#x20AC;&#x201C;29 years. The data obtained from these samples may not be representative which, thus, could affect the general applicability of the results.

(2) Negative reviews perceived The lack of consideration for the perception of negative reviews and attitudes of shopkeepers of logistics and other services has raised questions on the validity of the measured items. Moreover, online apparel goods are limited. The lack of consideration for this aspect may have resulted in data that are not representative,which, thus, could affect the general applicability of the results. Furthermore, international perception of negative reviews would be different so that cultural perspective to further research was needed.

REFERENCES [1]

Ahluwalia, R., Burnkrant, R. E., & Unnava, H. R. (2000). Consumer Response to Negative Publicity: The Moderating Role of

[2]

Bansal, H. S., & Voyer, P. A. (2000). Word-of-mouth processes within a services purchase decision context. Journal of service

[3]

Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reveiws. Journal of Marketing

[4]

Driver, B. L., Brown, P. J. & Peterson. G. L. (1991). Benefits of leisure. State Collage, PA: Venture

[5]

Guo, L. F. (2012). The study on the factors which influence on the helpfulness of the online review â&#x20AC;&#x201D; Based on the search goods.

Commitment. Journal of Marketing Research, 37(2), 203-214 research, 3(2), 166-177 Research, 43(3), 345-354

Unpublished master's thesis, Dongbei University of Finance and Economics, Dalian [6]

Hanson, W. A. (2000). Principles of Internet Marketing. South-Western College Publishing

[7]

Hennig-Thurau, T., & Walsh, G. (2003). Electronic word of mouth: Motives for and consequences of reading customer

[8]

Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion

[9]

Kalwani, M. U., & Silk, A. J. (1982). On the reliablity and predictive validity of purchase intention measures. Marketing Science,

articulation on the internet. International Journal of Electronic Commerce, 8(2), 51-74 platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38-52 1(3), 243-286 [10] Katz, E., & Lazarsfeld, P. F. (1970). Personal Influence: The part played by people in the flow of mass communications.

Transaction Publishers [11] Lapierre, J. (2000). Customer-perceived value in industrial contexts. Journal of Business & Industrial Marketing, 15(2/3), 122-145 [12] Liang, S., & Chen, S. (2006). Discussion on Influence Factors of negative word of mouth network. 10th Interdisciplinary management seminar, 5, 23-24 [13] Low, G. S., & Lamb, J. C. W. (2000). The measurement and dimensionality of brand associations. Journal of Product & Brand Management, 9(6), 350-370 [14] Mizerski, R W. (1982). An Attribution Explanation of the Disproportionate Influence of Unfavorable Information. General - 82 www.emj-journal.org

Information, 9(3), 301-10 [15] Nielsen (2010), Asia-Pacific countries report user habits of Internet users [16] Park, D. H., & Kim, S. (2008). The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic commerce research and applications, 7(4), 399-410 [17] Richins, M. L. (1983). Negative Word of Mouth by Dissatisfied Consumers: A Pilot Study. Journal of Marketing, 47(1), 68-78 [18] Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of Retailing, 80(2), 159-69 [19] Song, X. B., Cong, Z., & Dong, D. H. (2009). The impact of internet word-of-mouth on consumer’s product attitude. Chinese Journal of Management, 8(4), 559-566 [20] Number

of

Internet

Users

(2015).

Internet

Users

by

Country.

Retrieved

August

15,

2015,

from

http://www.internetlivestats.com/internet-users [21] Econsultancy (2014). 77% of UK shoppers consult reviews before buying online: report. Retrieved August 15, 2015, from https://econsultancy.com/blog/64406-77-of-uk-shoppers-consult-reviews-before-buying-online-report/ [22] Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter? — An empirical investigation of panel data. Decision Support Systems, 45(4), 1007-1016 [23] Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity Assessment. Beverly Hills, CA: Sage

AUTHORS Yuhua Cui is a Lecture in the School of

Jishun Niu is professor of Information

Business at Beijing Institute of Fashion

Management and Information System since

Technology. interests

Her

include

current

research

understanding

E-

commerce and consumer behavior

2005. Her course teaching was Electronic Commerce,

Management

Information

System, and Information Retrieval.

Rui Guo graduated from Beijing Institute of Fashion Technology.

- 83 www.emj-journal.org