In order to sympathise the issue of online reviews on consumer purchase behavior, more than 400 Taobao shops' online reviews are collected. Based on S-O-R model (Stimulus-Organism-Response Model), this newspaper studies the influence on consumer purchase behavior co-ordinate to online reviews of experience goods from a new perspective of consumer learning. Using SPSS 19.0 software for data assay and assumptions, statistical results show that the positive reviews, describing rating, flick reviews, appended reviews and cumulative reviews have an upshot on consumer purchase beliefs. The moderate reviews, negative reviews, logistics rating and service rating are not pregnant in the results. Finally, this paper puts forward suggestions and summary.

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Journal of Service Science and Management, 2015, 8, 419-424

Published Online June 2015 inorthward S ciRes. http://world wide web.scirp.org/journal/ jssm

http://dx.doi.org/x.4236/jssm.2015.83043

How to cite this paper: Mo, Z., Li, Y.-F. and Fan, P. (2015) Event of Online Reviews on Consumer Purchase Behavior. Journal

of Service Science and Management , 8 , 419-424. http://dx.doi.org/10.4236/jssm.2015.83043

Effect of Online Reviews on Consumer

Purchase Beliefs

Zan Mo, Yan-Fei Li , Peng Fan

School of Management, Yarduangdong University of Technology, Guangzhou, China

Email: 649319529@qq.com

Received 27 May 201 v; accepted 26 June 2015; published 29 June 201five

Copyright © 2015 past authors and Scientific Research Publishing Inc.

This work is licensed nether the Creative Commons Attribution International Licenseastward (CC Past).

http://creativecommons.org/licenses/by/4.0/

Abstract

In order to understand the effect of online reviews on consumer purchase behavior, more than than

400 Taobao shops' online reviews are collected. Based on S-O- R model (Stimulus-Organism -Re-

sponse Model), this paper studies the influencdue east on consumer purchase exist havior according to on-

line reviews of experience goods from a new perspective of consumer learning. Using SPSS 19.0

software for data analysis and assumptions, statistical results show that thdue east positive reviews, de-

scribing rating, pictureast reviews, appended reviews and cumulative reviews have an effect on con-

sumer purchase behavior. The moderate reviews, negative reviews, logistics rating and service

rating are not significant in the results. Finally, this newspaper puts forward suggestions and summary.

Keywords

Online Reviews, Consumer Learning, Experience Goods, Purchase Behavior

1. Introduction

The development of e-commerce websites led consumers to become accustomed to online shopping. According

to iResearch latest data due southhow that in Figure ane [ane] , in 201iv, the 3rd quarter of Chinese online shopping market

transaction sizeastward was 691.41 billion RMB, B2C market accounted for 44.two% and Tmall accounted for about sixty%

in B2C thousandarket. C2C market place tin can meet online consumers' differences and individual needs, which volition continue

to maintain a steady growth in the future. The 35th survey study released by CNNIC showed that [2] , up to Ddue east-

cember twenty14, the scale of Chinese Internet users reached 649 thouillion, of which mobile telephone users reached 557

million. Mobile shopping is becoming more and grandore popular.

Before consumers make a purchase in an due east-commerce website, they are air-conditioningcustomed to use online reviews inorth-

formation equally a basis to judge whether they purchase. Afterward consumers buy it, they will post their own comments

of the commodity in the e-commerce sites. In recen t years, the study of online reviews discusdue south usefulness and

Z. Mo et al.

Figure 1. Chinese B2C shopping due southites transaction size in the 3rd quarter of 2014.

credibility kainly. Thisouth paper collects the reviews information of experience koods in Taobao, to written report how o n-

line reviews affect purchase behavior during the purchasing process.

2. Theory Foundation

At that place are many kinds definitions of online reviews. Dissimilar scholars have different views. Park and Lee [3]

believe that online reviews are positive and negative reviews of the productsouth have been sold on the online shop-

ping mall. Mudambi and Southwardchuff [4] indicate out that online reviews are evaluation information of products and se r-

vice pasted on third- party sites and retailers, which created by the consumers. This paper believes that online re-

views are evaluation information about the different aspects of consumer products. With thidue south information, co n-

sumers can infer the quality of goods according to the reviews and experience, to reduce personal time and the

risk of purchase.

In general, quantitative study of how online reviews affect consumer purchasing behavior tin exist transformed

into a study of how online reviews touch on merchandise auctions. Past the research on online reviews and 1000ovie box

office revenues, some scholars found using sales to geasure consumer buying behavior was feasible in the

quantitative mensurate [five] . Therefore, this paper southwardtudies how online reviews affect consumer purchase behavior

via monthly commodity transaction records.

Online reviews information on Taobao is anonymous. Because of asymmetry, consumers tin not get all of the

information before buying. They have to collect online reviews existfore buying in order to raise awareness. On-

line reviews provide consumers with real shopping experience, and a reference to understand the products. Some

scholars believe that the driving strength of consumers to search online reviews are asymmetric data, lack

of available information, the cognitive perception authority and perceptual economic. Then searching online re-

views behavior of consumers before buying is an important step. Meanwhile, online reviews tin improve con-

sumer perception of shopping sites and productsouthward, attract consumersouthward caring potentially, and increase consumer

loyalty and sense of community to the site, which allows consumers to make better shopping decisions [half-dozen] .

As consumers' guide information, anonymous transmission of online reviews make more consumers are will-

ing to provideast their ain real-life feel, even if it is a negative one. But the sellers will take action to forcefulness

consumers to rewrite the negative reviews information, making some of the information cannot be trusted. Fur-

thermore, some Taobao sellers take harassment and retaliation to the customers who give negative reviews. So

the customers are agape to requite existent comments. Besides, brushing reputation behavior grandakes consumers cannot

distinguish which transaction is imitation, increasing judging difficulty according to the comments.

Consumer learning refers to the process that consumers acquire one thousandnowledge, eastxperience, skills, and imshow

their buying behavior constantly during the purchase. Consumers' attitudes are affected by the quantity and rat-

ings of online reviews. When consumers want to buy affair, they are hinted by the positive or negative com-

ments, they will like or dislike the goods. When high -quality reviews emerging, consumer impulse will continue

to get strengthened. When low-quality reviews emerging, consumer impulse is reduced. After their purchase, the

Z. Mo et al.

quality of goods is recognized past consumers. When at that place is demand to purchase again, they will make a repeat pur-

chase. Online reviews of credibility and usefulness adoption processouth tin can exist understood as a kind of consumer

learning behavior. So observing consumer buying process from the point of consumer learning getsouth very

necessary.

Watson, the founder of behavioral psychology, proposed "stimulus–response" yardodel. On the basis of it, One thousande-

hrabian proposed consumer behavior model, Stimulus-Organism- Response Model (SOR model) [7] . This mo d-

el indicates that the scenario tin can stimulate consumers' psychology, and then affect consumer behavior. This pa-

per studies the impact of online reviews on consumer purchasing behavior, shown inorth Figure two. The stimulus

comes from the rating and comment of online reviews. Review rating refers to positive review, moderate review,

and negative review of the goods. This paper analyzes how the quantity of review rating affects consumer beha-

vior. Review content refers to ii aspects: the quantity of reviews and shop reputation. The shop reputation i n-

cludes description rating, logistics rating and service rating. The quantity of reviews refers to the quantity of

pictures reviews, the quantity of additional reviews and the quantity of cumulative reviews.

3. Hypotheses

From an eastwardconomic indicate, Nelson divided goods into search appurtenances and experience goods. Search appurtenances means

the goods that consumers volition exist able to get accurate information before buying according to the information,

such as thousandobile phones, cameras, printers and so on. Experience goods refers to the one thousandoods that swallowr cannot

obtain air conditioningcurate information from product information existfore the purchase, such equally cosmetics, food, so on.

The results of foreign research evidence tchapeau experience good is more dependent on online reviews than search proficient,

and consumer tend to apply online reviews while buying experience goods [8] [ix] . Therefore, this paper studies

online reviews of experience getods.

Currently, the reviews systemdue south of different e-commerce sites aren't exactly the same. Just they are all inorth-

volved in logistics rating, services ratinthousand, description rating and comment review. On Taobao product pages,

websites offer consumers the hazard to post reviews with content in the form of numerical star ratings (ranging

from 1 to five stars). We can find a variety of information related to online reviews, such as pictures reviews, addi-

tional reviews, positive reviews, moderate reviews, negative reviews, cumulative reviews and others. Which one

volition take an impact on sales? This paper will discuss the factors one past one.

iii.1. Review Rating

Review rating of goods is the initial attitude of consumer, which is evaluated by the consumer on goods. The reastward-

views systems of B2C and C2C are slightly different. C2C review organization involves positive reviews, stylerate

reviews and negative reviews, while B2C does not have this option. In this newspaper, we study how the quantity of

these star rating on C2C website (Taobao) affect consumer purchase behavior. Consumers update the attitude of

goods according to the star ratings, which is one of the consumers learning process. Some study show tlid, the

quantity of positive reviews plays a significant role in the consumer decision one thousandaking, the quantity of waycharge per unit

reviews have no impact, the negative reviews have negative impact on consumers buying behavior.

Figure ii. SOR model of consumer purchase.

stimulus

organism

response

Review rating

Positive review

Moderate review

Negative review

Consumer

Review content

Shop reputation

Quantity of reviews

Buy/No buy

Z. Mo et al.

Therefore, the follattributable hypotheses are provided:

H1a: Positive reviews have a positive effect on consumer buying behavior.

H1b: Moderate reviews have no effect on consumer buying behavior.

H1c: Negative reviews have a negative consequence on consumer buying behavior.

3.2. Review Content

1) Store Reputation

Shop reputatiodue north refers to the content evaluation and star ratings givenorth by the consumers after the purchase.

Content evaluation may involve the comment of quality, price, service attitude, logistics and and then on. Consumers

tin also show pictures of the goods below the content form. In addition to the content eastvaluationorthward, customer

needs to give star ratings about logistics, services and description. Star rating rang from ane to 5 stars. A very

low rating (one star) indicates bad quality, bad service and slow logistics. A fiveery loftier rating (five stars) reflects

good quality, skilful service and fast logistics. Five stars are the all-time and one star is the worse. Shop reputation is

ane of the intangible assets and one of the attracting factors. Study has shown that thdue east star rating tin increase

consumer confidence.

Thereforeastward, the follattributable hypotheses are provided:

H2a: Description star rating positively affects consumer buying behavior.

H2b: Service star rating positively affects consumer buying behavior.

H2c: Logistics star rating positively affects consumer ownership behavior.

ii) Quantity of Reviews

Pictures are formed in the consumers' first review or additional reviews, which is one part of reviews. The re-

views containing pictures reflect the real quality of the goods, such as colour problem, specifications inconsistent

problem, or theast high quality, expert experience. The picture reviews reduce consumer' s gamble during buying expeast-

rience chiliadoods. In the consumer learning process, motion-picture show reviews indicates the real buying behavio r.

Consumer can complete the comment within 180 days. Later on that, the seller volition requite evaluation to the con-

sumer, too. Simply consumers have a second chance to append reviews, and sellers have another chance to eastxplain

as well. Additional reviews refer to the second reviews. Information technology tin can exist text or pictures, but does not bear on the seller's star

rating. Boosted reviews reflect the consumer experience better. Thus additional reviews reflect a existent situation

after purchase. The quality problem of some commodities will not appear in a short time. Via additional reviews,

consumer can understand the quality and durability of goods. Southwardome scholars use complex internetpiece of work approach to

study additional reviews of lotion in Taobao, and found that the time presents long-tailed distribution with a

specific regularity.

Cumulative reviews are reviews posted within a month. The reviews may involve color, quality, specification,

logistics, customer service and other factors. The reviews can be done a long tirade, also tin be a short give-and-take

evaluation, such as good or bad. Cumulative review is adue north important channel for consumer to understand adeptsouthward

information before buyinchiliad. The hellogh richness information tin can help consumers understand product information

deeply. Some written report testify that the influence of positive reviews replaces the influence of cumulative reviews.

Only another study found that the quantity of online reviews affect the willingness of consumers to buy on the

network.

In summary, the following hypotheses are provided:

H3a: The additionorthwardal reviews have a positive impact on consumer buying behavior.

H3b: The moving picture reviews accept a positive impact onorth consumer buying behavior.

H3c: The cumulative reviews have a positive impact on consumer buying behavior.

4. Data Drove, Analysis and Results

Based on the post-obit reasons, this paper selects cleanser every bit the research object. Offset, cleanser is an experience

product, consumer buy it for personal employ or give others as gifts, avoiding comparisons and other emotional fac-

tors to make a purchase. Second, this product is a daily employ product. Consumers have dissimilar feel with

each other, excluding the impact of consumers' personal ironelings about thursdayeastward make effect.

Taobao website offers 30 days of transaction records and reviews records. So this newspaper collects 30 days sales

on Taobao for the study. We select online data from November 15, 201four to December 15, 2014 as data sample,

including 440 shops' sales information of Shiseido cleanser on Taobao. The total due northumber of reviews is 218 954 .

Removing invalid evaluation, data sample involvesouth 434 shops' information.

Z. Mo et al.

The descriptive statistics for all variables are included in Table 1 . The yardinimum value of positive reviews,

moderateast reviews, negative reviews is 0. Different southwardhops have different sales. Some shops sold this product little

and no one gives reviews. So the minimum value is 0. The hateful of moderate reviews and negative reviews is

less than 3. Becauseast consumers don' t often give chiliadoderate review or negative review, so the hateful is small. The

minimum value of description ratingrand, services rating and logistics rating are iv.5, the maximum is v, the mean is

almost iv.viii (out of 5), showing the three ratinm of 434 southhops have fiddling divergence. The minimum value of pictures

reviews, additional reviews and cumulative reviews are 0. The meanorthward of picture reviews and additional reviews is

well-nigh ten. It costs consumers more fourth dimension and effort to post pictures and additional reviews. If the product does not

appear whatever problems, consumers do non give additional reviews and pictures specially.

The results of the regression analysis are included in Table 3 . From the regression result of one thousandodel 1, the

analysis of the chiliadodel indicates a good fit, with a highly significant likelihood ratio ( p = 0.000), and R -square

value of 0.888, shown in Table two . In H1, we hypothesized that positive reviews have a positive bear on, mo d-

erate reviews take no impact, and negative reviews have negative impact. We fin d strong support for H1a. Posi-

tive review is a loftierly significant (p < 0.000) predictor of consumer buying behavior. The results too provide

back up for H1b, which hypothesizes moderate reviews have no impact on consumer buying behavior. The re-

sults don't provide support for H1c. Then the hypothesis that negative reviews have negative impact on consumer

buying existhavior is not significant in this study.

Table 1. Descriptive statistics for full sample.

Variable N Minimuk Maximum Mean Standard Fault

Positive reviews 407 0 25084 221.04 1344.989

Moderate reviews 407 0 371 ii.67 19.424

Negative reviews 407 0 241 i.5 12.68

Description star rating 434 4.four 5 4.814 0.0695

Services star rating 434 4.iv five 4.832 0.0689

Logistics star rating 434 4.v 5 4.826 0.0748

Picture reviews 434 0 929 11.89 68.739

Cumulative reviews 434 0 26476 381.77 1936.304

Additional reviews 434 0 919 ten.44 51.202

Monthly sales 434 7 11189 161.99 669.49

Table 2. Model summary.

Model R R Square Adapted R Square Std. Error of the Estimate Sig. F Durbin-Watson

one 0.888 0.789 0.789 314.424 0 1.988

2 0.874 0.765 0.762 326.296 0.029 ane.943

Table iii. Regression output for full sample.

Model

Unstandardized Coefficients Standardized Coefficients

t-value Sig.

B Standard Mistake

1

(Constant) 56.193 xv.795 three.558 0

Positive reviews 0.452 0.012 0.888 38.941 0

ii

(Constant) 2431.711 1088.115 ii.235 0.026

Boosted reviews x.252 0.652 0.784 fifteen.73 0

Motion-picture show reviews 2.614 0.564 0.268 4.635 0

Cumulative reviews − 0.065 0.017 -0.189 − 3.786 0

Description star rating − 494.982 225.956 -0.051 − two.191 0.029

Z. Mo et al.

From the regression results of model 2, the analysis of the model signalsouthward a good fit, with a highly significant

likelihood ratio (p = 0.029), and an R-square value of 0.847, shown in Table 2 . In H2, nosotros hypothesized that ddue east-

scription star ratingrand positively affected consumer buying behavior, service star rating positively affected con-

sumer buying behavior, logistics star rating positively afflicted consumer buying behavior. Nosotros discover describing

rating negatively affectdue south consumer buying existhavior. H2a gets an opposite support. Service star rating and logis-

tics star rating have no effect idue north this written report. H2b and H2c get no support. In model 2, we find strong support for

H3a and H3b. The additional reviews and picture reviews are highly significant (p < 0.000) predictor of conorthward-

sumer buying behavior. In H3c, picture reviews have an opposite effect. Cumulative reviews negatively affect

consumer ownership behavior.

v. Conclusion

This paper discusses the impact of online reviews on consumer buying behavior. It collectdue south a brand of cleanser

on Taobao website equally the inquiry object. The results show that thursdaye influential factors of online reviews on co n-

sumer buying behavior include positive reviews, description rating, picture reviews, additional reviews and cu-

mulative reviews. The four factors, moderate reviews, negative reviews, service star rating and logistics star rat-

ing, are not significant in this research. Therefore, the sellers can take the incentives for consumers to make pos-

itive reviews and loftier quality reviews in the sales process. For example, cash, coupons, fellow member points are good

incentive to encourage consumers to mail service additional reviews and show picturetures actively. So the sellers must also

amend the quality of their goods in society to increase comment information. The conclusion of this paper can

assist consumers make decisions quickly based on online reviews in a short fourth dimension. It can also help sellers to grasp

the importance of online reviews, correct deficienciedue south timely and provide a referencdue east for the adjustment of mar-

keting strategy. Besides, this is a cantankerous-sectional study and it fails to investigate the longitudinal effect of online

reviews on consumer purchase existhavior. Other scholars canorthward study the longitudinal effect in time to come research.

Fund

This study is funded by National Natural Science Foundation of China: 71171062.

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http://dx.doi.org/10.1007/s10834-004-5490-0

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  • Etta Y. I. Chen
  • Richard Widdows

This report investigates consumers' importance evaluation and usage of the Net as an data source, compared with other traditional information sources. The primary issue is the extent to which feel products can be transformed then as to be searchable on the Internet. The results of a spider web-based survey showed that consumers of experience products tended to employ more than online information than those of search products. Online information sources from other consumers and neutral sources were perceived to exist more important and were used more often by the consumers of experience products; whereas retailer/manufacturer websites were perceived to exist more useful by consumers of search products. Perceived usefulness, perceived ease-of-utilize, market mavenism, and usage of offline information sources were likewise positively related to the usage of online information.

There are growing interests in understanding how word-of-mouth (WOM) on the Internet is generated and how it influences consumers' purchase decisions at retail outlets. A unique attribute of the WOM issue is the presence of a positive feedback machinery between WOM and retail sales. We characterize the process through a dynamic simultaneous equation organization, in which we separate the effect of online WOM as both a forerunner to and an outcome of retail sales. We use our arroyo to the movie industry, showing that both a picture show'south box function revenue and WOM valence significantly influence WOM book. WOM volume in turn leads to higher box office functioning. This positive feedback machinery highlights the importance of WOM in generating and sustaining retail revenue.

This written report investigates consumers' usage of online recommendation sources and their influence on online production choices. A three (websites) × 4 (recommendation sources) × 2 (products) online experiment was conducted with 487 subjects. Results indicate that subjects who consulted product recommendations selected recommended products twice as often as subjects who did not consult recommendations. The online recommendation source labeled "recommender arrangement," typical of the personalization possibilities offered by online retailing, was more influential than more traditional recommendation sources such as "homo experts" and "other consumers". The type of product as well had a meaning influence on the propensity to follow product recommendations. Theoretical and managerial implications of these findings are provided.

  • Susan M. Mudambi Susan M. Mudambi
  • David Schuff

Customer reviews are increasingly available online for a broad range of products and services. They supplement other information provided by electronic storefronts such equally product descriptions, reviews from experts, and personalized communication generated by automatic recommendation systems. While researchers accept demonstrated the benefits of the presence of client reviews to an online retailer, a largely uninvestigated issue is what makes customer reviews helpful to a consumer in the process of making a purchase conclusion. Drawing on the paradigm of search and experience goods from information economic science, we develop and test a model of customer review helpfulness. An assay of 1,587 reviews from Amazon.com across 6 products indicated that review extremity, review depth, and production blazon bear on the perceived helpfulness of the review. Production type moderates the effect of review extremity on the helpfulness of the review. For feel goods, reviews with extreme ratings are less helpful than reviews with moderate ratings. For both product types, review depth has a positive outcome on the helpfulness of the review, simply the product type moderates the effect of review depth on the helpfulness of the review. Review depth has a greater positive effect on the helpfulness of the review for search goods than for experience goods. We discuss the implications of our findings for both theory and practice.

  • Albert Mehrabian
  • James A Russell James A Russell

Identifies and measures relevant variables (due east.yard., color, heat, light, and sound) involved in environmental psychology and fits them into a systematic framework. It is proposed that ecology stimuli are linked to behavioral responses by the primary emotional responses of arousal, pleasure, and authorisation. (31 p ref) (PsycINFO Database Tape (c) 2012 APA, all rights reserved)

  • Nanda Kumar
  • Izak Benbasat Izak Benbasat

Recommendations and consumer reviews are universally best-selling as significant features of a concern-to-consumer website. However, considering of the well-documented obstacles to measuring the causal impact of these artifacts, there is still a lack of empirical evidence demonstrating their influence on 2 of import outcome variables in the shopping context: perceived usefulness and social presence. To examination the being of a causal link between information technology (Information technology)-enabled support for the provision of recommendations and consumer reviews on the usefulness and social presence of the website, this study employs a novel arroyo to generate the experimental conditions by filtering the content of Amazon.com in real time. The results show that the provision of recommendations and consumer reviews increases both the usefulness and social presence of the website.

The 35th Statistical Report on Red china Net Network Development

CNNIC (2015) The 35th Statistical Report on China Net Network Development [EB/OI]. http://news.xinhuanet.com/politics/2015-02/03/c_127453226.htm

Consumers' Online Information Search Behavior and the Phenomenon of Search vs. Experience Products

  • L T Bei
  • E Y Chen
  • R Widdows

Bei, Fifty.T., Chen, Due east.Y. and Widdows, R. (2004) Consumers' Online Data Search Beliefs and the Phenomenon of Search vs. Experience Products. Periodical of Family and Economic Problems, 25, 449-467. http://dx.doi.org/x.1007/s10834-004-5490-0