The influence of online reviews on consumers’ purchase decision ——an empirical study

Scientific Journal of E-Business October 2014, Volume 3, Issue 3, PP.12-18

The Influence of Online Reviews on Consumers’ Purchase Decision ——An Empirical Study Hualin Wang Jiangxi University of Finance & Economics, Industrial Clusters and Enterprise Research Center, Nanchang China, Email: wanghualin2002@163.com

Abstract There are lots of academic researches on the impact of information on consumer behavior, but researches about the influence of online reviews on consumer purchasing decisions are few. Online reviews as a new way of online word of mouth have more advantages. So it is meaningful for us to study it deeply. Academically, it can make up gaps for online reviews on consumer purchase decision-making mechanism. Practically, it also can be better guidance for online marketing.

Keywords: Online Reviews, Trust, Consumer Purchasing Decision, Empirical Study

1 INTRODUCTION The introduction of online reviews has provided a way for consumers to share buying experience, however, other related problems follow. First, the open network brings information overload. Since the network features on timely, universally and interactive exchanges, it allows consumers to comment on same products at the same time. While at the same time, their comments will be kept in the webpage for a long time. On the one hand, a variety of reviews provide consumers with comprehensive information. On the other hand, faced with such a number of comments, their decision-making ability will be disturbed. “Information overload” has become a problem we had to face.

Moreover, the authenticity of online reviews has been questioned. In fact, online reviews are comments made by consumers about products and their using experience. However, by using the powerful impact of online reviews, some businessmen published untrue information to interfere consumers’ decisions.

2 INFLUENCE FACTORS ON ON-LINE REVIEWS Although most studies have shown the significant impact of online reviews on consumers, scholars hold different opinions and put forward credible proofs on factors that have influenced the effectiveness of online reviews.

2.1 Factors on Reviewers.

1) Specialty A professional reviewer is the one who equipped with specialized knowledge and purchasing experiences. Under the context of network, consumers are more willing to believe in comments made by professional reviewers. Gilly (1998) deepened the view that comments remarked by a professional reviewer will produce a good impact on products. It is said thatconsumers with a high specialties will make a more reasonable choice and refrain from bad information. Therefore, their information will be more trustworthy and reliable to consumers.

2) Reliability Reliability is receivers’ acceptance of information from reviewers.It is said that the higher degree of recognition, the larger degree of reliability. Geddes (2004) believes that a good remark from acquaintances will promote sales.

Commonly, people are more inclined to accept information from reliable sources (basically from acquaintances).

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2.2 Factors on Receivers 1) Disposition to trust Trust propensity is people’s willingness to trust other people. Michale (1995) holds that people with high trust propensity are more likely to believe in others.A study shows that comments from third parties, especially from consumers, win more trust than others. Thus, we can infer that consumers with stronger disposition to trust are more likely to accept comments on web, thus contributing to buying behaviors.

2) Herd Behavior Sherif and Ascli are the first who discovered the herd theory. They think that consumers exhibit similar buying tastes in order to win trusts from their groups. While Cao Hongjian and Yao Binghong (2003) argue that consumers will take the buying experience of others as references and try to be consistent with the vast majority of people.After empirical studies, we found that the higher the consistency of comments posted online, the more likely for consumers to follow public opinions. Zhou Jingjing (2010) found that the herd behavior enjoys a remarkable effect on people’s purchase and decision-making process.

3) Product Involvement Product involvement refers to consumers’ emphasis on products. This definition is out of personal perception. Flynn insists that consumers with a high product involvement will actively search for product information to make an ideal buying decision. While on the other hand, consumers with a low product involvement are unwilling to compare products, thus, they are more likely make a rather simplified buying decisions.

2.3 Factors on Evaluation Content 1) Quality and Quantity The quality of comments refers to the reliability, authenticity, adequacy and relevance of reviews. When a buyer reads comments, he will intuitively feel the quality of the comments. Park (2007) holds that the quality and quantity of comments largely influence people’s buying willingness. He demonstrates from different angles that the quality and quantity of online reviews exert positive effect on consumers’ purchase behavior.

2) Time-Effectiveness Time-effectiveness refers to the time a review is posted. It also concerns about whether a review covers the latest information on products or services. For example, an old review has a low time-effectiveness, thus, it brings little value on consumers’ decision-making process.Zheng Xiaoping and Wang Zhenzhen have studied the influence of time-effectiveness on online reviews.

Most studies on online reviews are focused on basically three areas: first, the professional and reliability of reviewers; second, the trust propensity, herd behavior and product involvement of receivers; third, the quality and quantity as well as the time-effectiveness of reviews.

3 MODEL BUILDING AND STUDY DESIGN This paper focuses on studying factors affecting consumers’ purchase decisions. These factors are divided into the following four: first, whether comments are given by opinion leaders; second, brand involvement and emotional tendencies of receivers; third, the quality of comments; the similarity between reviewers and publishers. Trust is an intermediary variable that affects consumers’ buying decisions. The model of this study is shown in Figure 3-1.

3.1 Model Building 3.2 Study Design 1) Our Online Shoppers Reflect The Following Characteristics.

In terms of gender, women are no longer the main strength, since men accounted for 62.6% of the total and increased - 13 www.sjae.org

quarter-on-quarter by 1.7%. In terms of age, consumers between 18 to 40 years old accounts for 63.4%. They have become the backbone of the network shopping. While in terms of education background, consumers with college degree or above account for 63.4%. It seems that the highly educated group still occupies a very high proportion in online shopping. Moreover, in terms of occupation structure, employees account for 37.1%, while students increase to 14.1%. From the above data, we can see that the 18-40 year old people with high education background are the main population in our online shopping.

2) Questionnaire Design The first part of our questionnaire will be primarily measured in a particular scenario. It focuses on finding out interviewers’ understanding and awareness of each item in the questionnaire. In addition, we design questionnaire based on small-scale interview and literature reading. Each variable measurement adopts the 5-Likert scale, namely “totally disagree”, “ somewhat disagree”, “general”, “somewhat agree”. Moreover, the second part of our questionnaire is the basic information of interviewers, including gender, age, education level, occupation, and purchase expense in each month.

H1a

H2b

Opinion leaders H2a Brands involvement

H3b Trus t

H3a

H6

Qualityof comments

... ..

H4a Emotional tendency

Decisions made by

consumers

H4b

H5a H5b

Emotional tendency

FIGURE 3-1 THEORETICAL MODEL ON THE IMPACT OF ONLINE REVIEWS ON CONSUMERS’ BUYING DECISIONS

4 DATA ANALYSIS AND INSPECTION We have distributed 260 questionnaires, among which 253 were recovered. The recovery rate is 97.3%. Next, we will analyze all of these questionnaires to find out their reliability and validity.

4.1. Statistical Description of Variables Statistical description of variables is shown in Table 3-1.

3-1 VARIABLE DESCRIPTIVE STATISTICS N

Minimum Value

Maximum Value

Mean Value

Standard deviation

Opinion leaders

253

1.00

5.00

3.4822

.77067

Brands involvement

253

1.33

5.00

3.5665

.77867

Quality of comments

253

1.17

5.00

3.3712

.80717

Emotional tendency

253

1.00

5.00

3.6653

.85132

Similarity

253

1.00

5.00

3.2144

.79664

Trust

253

1.00

5.00

3.3251

.94873

Decisions made by consumers

253

1.00

5.00

3.3182

1.08129

N

253

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4.2 Reliability Analysis Reliability analysis refers to the consistency and stability of the measurement results after repeated tests. This paper aims to study the inherent reliability of the questionnaire, thus finding out whether a group of questions are measuring the same concept. Usually, we use Cronbach’ Alpha index to value the inherent reliability of the questionnaire. Due to the deletion of specific dimensions in previous questionnaires, we need to re-measure the reliability of each variable, thus ensuring the consistency of results.The outcome of the reliability analysis is shown in Table 3-2.

TABLE 3-2.RESULT OF THE RELIABILITY ANALYSIS

Variables Opinion leaders Brands involvement Quality of comments Emotional tendency Similarity Trust Decisions made by consumers Overall reliability

Questions 4 3 6 3 4 4 4 28

Cronbach's Alpha Index .813 .728 .846 .770 .729 .891 .837 .836

As shown in Table 3-2, most of variables’Cronbach’ Alpha indexes are above 0.7, which indicates a good reliability of each variable.

4.3 Normal Distribution Inspection We carry out a normal distribution inspection on each variable. Normally, we adopt a kurtosis and skewness inspection to do the normal distribution test. Also, we consider the data is assumed to obey the normal distribution as long as the absolute value of skewness is les than or equal to 2, while the absolute value of kurtosis is less than or equal to 5. Results of the normal distribution test are shown in Table 3-3.

TABLE 3-3 DESCRIPTIVE STATISTICS SCALE OF SKEWNESS AND KURTOSIS

N

Skewness

Kurtosis

Statistics

Statistics

Standard error

Statistics

Standard error

Opinion leaders

253

-.248

.153

.111

.305

Brands involvement

253

-.175

.153

-.501

.305

Quality of comments

253

-.375

.153

-.036

.305

Emotional tendency

253

-.571

.153

.250

.305

Similarity

253

-.323

.153

-.117

.305

Trust

253

-.577

.153

.389

.305

Decisions made by consumers

253

-.459

.153

-.242

.305

Effective N (listing status)

253

As shown in Table 3-3, the skewness and kurtosis of each variable are within a specified range. All of variables meet normal distribution requirements.

4.4 Correlation Analysis Correlation analysis is a common method used for studying the relationship between different variables. If the coefficient is positive, it indicates that the relationship between variables is positive. On the contrary, if the coefficient is negative, it means that the relationship between variables is negative. Moreover, if the coefficient is zero, it indicates that there is no relationship between the two variables. A correlation analysis requires the dependent variable to be normally distributed. According to results of the normal distribution test, we directly use pearson to do related analysis. The results of the correlation analysis between different variables and factors like trust and - 15 www.sjae.org

consumers’ buying decisions are shown in Table 3-4 and Table 3-5.

TABLE 3-4 CORRELATION ANALYZE BETWEEN VARIABLES AND TRUST

Variables Online opinion leaders Brands involvement Quality of comments Emotional tendency Similarity

r 0.449 0.553 0.545 0.513 0.760

P 0.000 0.000 0.000 0.000 0.000

As shown in Table 3-4, there are exist significant correlations between variables and trust.

TABLE 3-5 CORRELATIVE ANALYZE BETWEEN VARIABLES AND CONSUMERS

Variables Online opinion leaders Brands involvement Quality of comments Emotional tendency Similarity Trust

r 0.384 0.389 0.433 0.446 0.188 0.671

P 0.000 0.000 0.000 0.000 0.003 0.000

As shown in Table 3-5, there are exist significant correlations between variables and trust.

4.5 Regression Analysis To better understand the causality and the impact mechanism among variables, this paper use the hierarchical regression approach to study the relationship between each variable.

(1)The relationship between online reviews and decisions made by consumers.

The relationship between online reviews and decisions made by consumers is shown in Table 3-6.

TABLE 3-6 REGRESSION EFFECTS OF ONLINE REVIEWS ON DECISIONS MADE BY CONSUMERS

Non-standardized coefficient Model

Standardized coefficient

t

Sig.

-2.431

.016

.198

3.456

.001

.084

.137

2.255

.025

.284

.079

.212

3.597

.000

Emotional tendency

.294

.076

.231

3.860

.000

Similarity

.190

.071

.140

2.682

.008

B

Std. Error

(Constant)

-.970

.399

Opinion Leader

.278

.080

Brand Involvement

.190

Quality of reviews

Beta

1

As shown in Table 3-6, the sig. values between each dimension and decisions made by consumers are less than .05, which signals a significant positive relationship.

(2)Relationship between online reviews and trust Regression effects of online reviews on trust are shown in Table 3-7.

As shown in Table 3-7, except similarity, each dimension remains in a significant positive relationship with trust.

(3)Relations between the following three: online review, trust and consumers’ willingness to buy.

The overall regression results of online reviews, trust and consumers’ willingness to buy are shown in Table 3-8.

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TABLE 3-7 REGRESSION EFFECTS OF ONLINE REVIEWS ON TRUST.

Non-standardized coefficient

Model 1

B

Std. Error

(Constant)

-.628

.303

Standardized coefficient

t

Sig.

-2.072

.039

Beta

Opinion Leader

.222

.061

.180

3.633

.000

Brand Involvement

.335

.064

.275

5.236

.000

Quality of reviews

.339

.060

.288

5.639

.000

Emotional tendency

.268

.058

.240

4.627

.000

Similarity

-.043

.054

-.036

-.794

.428

TABLE 3-8 REGRESSION RESULTS OF ONLINE REVIEWS, TRUST AND CONSUMERS’ WILLINGNESS TO BUY

Model

1

Non-standardized coefficient

Standardized coefficient

t

Sig.

-1.620

.106

.098

1.897

.059

.078

-.016

-.278

.781

.074

.053

.954

.341

.125

.070

.098

1.789

.075

.216

.062

.160

3.480

.001

.631

.074

.554

8.582

.000

B

Std. Error

(Constant)

-.573

.354

Opinion Leader

.138

.072

Brand Involvement

-.022

Quality of reviews

.070

Emotional tendency Similarity Trust

Beta

In terms of the relationship between each variable, we can see from Tables that there exists a positive relationship between similarity and trust, however, the relationship between other variables and decisions made by consumers are not significant.

5 CONCLUSION In this paper we have discussed the influence of online reviews on consumers’ purchase decisions. Meanwhile, we have studied the role of trust. Then, we initially established the affecting mechanism of online reviews on consumers purchase decisions. In the course of analysis, we have examined each hypothesis and proved the correlation between variables and trust. The results of the hypothesis test are shown in Table 3-9.

Based on the result of the above empirical test, we have drawn the following conclusions: TABLE 3-9 RESULT OF EMPIRICAL TEST

Hypothesis H1a: Opinion leader significantly affect trust.

H1b: Opinion leader significantly influence consumers’ buying decisions.

H2a: Brands involvement significantly affects receivers’ trust on a product.

H2b: Brands involvement significantly affects consumers’ buying decisions.

H3a: The quality of comments significantly affects consumers’ trust on a product.

H3b: The quality of comments significantly affects consumers’ buying decisions.

H4a:Emotional tendency significantly affects consumers’ trust on a product.

H4b: Emotional tendency significantly affects consumers’ buying decisions.

H5a: Similarity significantly affects consumers’ trust on a product.

H5b: Similarity significantly affects consumers’ buying decisions.

H6: Receivers’ trust on online reviews significantly affect consumers’ buying decisions.

Established or not √ √ √ √ √ √ √ √ √ √ √

(1)Review publishers are opinion leaders. While receivers’ brands involvement, quality of comments and emotional tendency has a significant impact on consumers’ trust on a product. Moreover, the similarity between publisher and - 17 www.sjae.org

receivers also acts actively on consumers’ buying decisions.

(2)Review publishers are opinion leaders. While receivers’ brands involvement, quality of comments and emotional tendency has a significant impact on consumers’ trust on a product. Moreover, the similarity between publisher and receivers also acts actively on consumers’ trust on a product.

(3)Trust acts as an important intermediary in online reviews and consumers’ buying decisions. Also, it has a significant mediating effect and acts like an entirely intermediary.

REFERENCES [1]

ZhengJia. Marketing Strategy of the Integrated Network Basing on Information Exchange [D]. Jinlin University, 2011.

[2]

Cao Yuan. Study on the Influence of Online Dissecting Information on Information Adoption [D]. Southwestern University of Finance and Economics, 2012.

[3]

GuoLinfang. Factors that Influence the Effectiveness of Online Reviews– Based on Searching Products [D].Dongbei University of Finance and Economics.

[4]

Yang Qing.Study of Consumers’ Delivery of Trust on Online Shops [D].Fudan University, 2005.

[5]

Chin-Lung Lin, Sheng-Hsien Lee, Der-JuinnHorng.The effects of online reviews on purchasing intention: The moderating role of need for cognition[J]. Social behavior and personality. 2011.39(1):71-82

[6]

Stephan Ludwig, Ko de Ruyter, Mike Friedman, Elisabeth C.Brüggen, Martin Wetzels, & Gerard Pfann. More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates[J]. Journal of Marketing.2013.2(77):87-103

AUTHORS Hualin Wang, male. Han nationality. Associate professor. Specialize in marketing and e-commerce. Ph. D. graduates from the Wuhan University of Public Economic Management. Wanghualin2002@163.com

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