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European Journal of MarketingThe impact of online user reviews on cameras sales

Lin Zhang Baolong Ma Debra K. Cartwright

Article information:To cite this document:Lin Zhang Baolong Ma Debra K. Cartwright, (2013),"The impact of online user reviews on cameras sales", EuropeanJournal of Marketing, Vol. 47 Iss 7 pp. 1115 - 1128Permanent link to this document:http://dx.doi.org/10.1108/03090561311324237

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Jan Ahrens, James R. Coyle, Michal Ann Strahilevitz, (2013),"Electronic word of mouth: The effects of incentiveson e-referrals by senders and receivers", European Journal of Marketing, Vol. 47 Iss 7 pp. 1034-1051 http://dx.doi.org/10.1108/03090561311324192

Hua-Ning Chen, Chun-Yao Huang, (2013),"An investigation into online reviewers' behavior", European Journal of Marketing, Vol. 47 Iss 10 pp. 1758-1773 http://dx.doi.org/10.1108/EJM-11-2011-0625

Peter De Maeyer, (2012),"Impact of online consumer reviews on sales and price strategies: a review anddirections for future research", Journal of Product & Brand Management, Vol. 21 Iss 2 pp. 132-139 http://dx.doi.org/10.1108/10610421211215599

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The impact of online user reviewson cameras sales

Lin ZhangSchool of Business, Truman State University, Kirksville, Missouri, USA

Baolong MaSchool of Management and Economics, Beijing Institute of Technology, Beijing,

 PR China, and 

Debra K. CartwrightSchool of Business, Truman State University, Kirksville, Missouri, USA

AbstractPurpose  – The purpose of this research is to help better understand the impact of online user reviewson sales of search goods.

Design/methodology/approach  – This research is based on digital camera sales data collectedfrom amazon.com and two studies are included in this research. The first study is based on a staticmodel and sample data from one time stamp. The second study is based on two sample data collectedfrom two different time stamps, and a dynamic model is proposed.

Findings – The results from the first study reveal that the average online customer review, thenumber of online reviews, the price and the camera’s physical properties such as the number of pixelsand the optimal zoom number (but not LCD screen size) have significant influence on digital camerasales. The results from the second study show that the sales from the previous period are an importantindicator for future sales. In addition, change in price, change in average online review rating andchange in the total number of online reviews are all significantly associated with future sales.

Research limitations/implications  – The research reveals that there is a significant relationshipbetween the online user review and sales of search goods, and the influence of online user reviews onsearch goods sales is different from that on experience goods. It also recognizes that the productspecifications influence the sales of search goods. In addition, the research on search goods shows thatprice at the specific time and price changes are significant factors affecting sales.

Practical implications   – The research indicates that retailers should provide channels for, andencourage, customer online reviews for search goods to improve sales. It is also beneficial for onlineretailers to provide detailed product attributes to help their customers make the purchase decision.Carefully designed and executed price promotions could also be effective ways to improve sales of searchable goods.

Originality/value – This study is one of the first attempts to investigate the impact of online userreviews on sales of search goods.

Keywords Online customer review, Word of mouth, Technology products, Search goods,

Marketing strategy, Sales strategies, Cameras

Paper type  Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0309-0566.htm

This research was partly supported by the National Natural Science Foundation of China underGrant 71002102 and the Key Project Cultivation Fund of the Scientific and Technical InnovationProgram, Beijing Institute of technology (2011DX01001).

The impact ofonline user

reviews

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Received 28 December 2011Revised 13 April 2012

Accepted 11 June 2012

European Journal of Marketing

Vol. 47 No. 7, 2013

pp. 1115-1128

q Emerald Group Publishing Limited

0309-0566

DOI 10.1108/03090561311324237

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IntroductionIn the internet era, online user reviews have emerged as an important source of information to consumers, substituting and complementing other forms of offlineword-of-mouth (WOM) communication about product quality and customer service.

One of the advantages of online WOM, compared to the classical offline WOM, is itsaccessibility: individuals can make their opinions easily accessible to other internetusers, and at the same time, potential new customers can easily access the onlinereviews from existing customers (Dellarocas, 2003). Past research has shown that theonline user review has a significant impact on customers’ purchase behavior (Chevalierand Mayzlin, 2006; Clemons  et al., 2006), and therefore influence product sales (Chenand Xie, 2008).

Most of the existing research about online user reviews has focused on experiencegoods (Chevalier and Mayzlin, 2006; Clemons  et al., 2006), while little effort has beenmade focusing on search goods. In an attempt to explore the importance of online userreviews on more general products, the current research will investigate whether onlineuser reviews influence the sales of search goods, and if so, how it works and whetherthere is any difference between their influence on search goods and experience goods.Specifically, using data collected from a major online sales website, we make an initialattempt to investigate the impact of online user reviews on digital camera sales.

The remainder of this paper is organized as follows. We begin with a review of therelevant literature about WOM, online user reviews, search goods and experiencegoods, followed by the introduction of two empirical models and the data used in thesestudies. The next section reports the results followed by our discussion andconclusions.

Literature reviewOnline user reviews and traditional WOM 

The basic definition of WOM is “informal advice” or communication about products,services, and firms that can be spread from one consumer to another in person or via acommunication medium (East   et al., 2007). WOM research has attracted continuousacademic and practitioner interest (Anderson, 1998; Goldenberg   et al., 2001; Huanget al., 2011; Stokes and Lomax, 2002). Because WOM is initiated by customersindependent of the market, it is perceived to be more reliable and trustworthy thanfirm-initiated communications. As one of the most powerful marketing communicationchannels, it has been widely accepted that WOM is closely related to a firm’s success(Godes and Mayzlin, 2004).

The development of the internet and information technology provides consumers anonline communication channel to share their product evaluations. Associated with thisprocess, the online consumer product review emerges as a new market phenomenon

and is playing an increasingly important role in influencing consumers’ purchasedecisions (Chen and Xie, 2008). Compared to traditional WOM, the influence of which istypically limited to a local social network (Shi, 2003), the impact of online consumerreviews can reach far beyond the local community, because consumers all over theworld can easily access a review via the internet. In addition, traditional WOM isgenerally not a direct decision variable for the product sales (Chen and Xie, 2008), butrecent research has found a direct connection between online user reviews and productsales (Chevalier and Mayzlin, 2006). For example, existing studies have shown that

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online user reviews have a significant positive impact on experience goods like books(Chevalier and Mayzlin, 2006), beer (Clemons  et al., 2006), movies’ box office revenues(Duan  et al., 2008) and hotel business (Ye  et al., 2009).

 Experience goods and search goodsExperience goods are evaluated predominately by subjective experience, andcustomers require sampling or purchase in order to evaluate product quality; whilesearch goods are mostly evaluated by objective properties and the customers do notrequire interaction in order to evaluate the product (Nelson, 1970, 1974). Examples of experience goods include music, books and beer; and examples of search goods includecameras (Nelson, 1970).

With the development of the internet and the prevalence of online product reviewinformation, all attributes of search and experience goods are searchable and thetraditional distinction between experience goods and search goods has been reduced(Huang  et al., 2009). However, recent research has found that the distinction between

search goods and experience goods is still valid (Mudambi and Schuff, 2010) due to thedifferent ways the product related information is accessed and processed (Huang  et al.,2009). Compared to consistent findings that the online review affects sales of experience goods (Chevalier and Mayzlin, 2006; Clemons et al., 2006; Duan et al., 2008;Huang et al., 2009; Mudambi and Schuff, 2010; Ye  et al., 2009), the limited research onsearch goods shows mixed results. For example, Huang  et al. (2009) studied customers’online purchase behavior and concluded that the online consumer review is notimportant for search goods, while Mudambi and Schuff’ (2010) showed that onlinecustomer reviews are helpful for search goods. However, neither study used actualsales data in their research. The purpose of this study is to examine the connectionbetween actual sales of search goods and online user reviews.

Online user reviews and experience goodsThe impact of online customer reviews on experience goods has been extensively studiedand it is widely accepted that online customer reviews can help boost the sales of experience goods. However, there are still some inconsistencies about which aspect of theonline user review works. For example, by comparing the sales data and customerreviews from Amazon.com and Barnesandnoble.com and using a differential model tofocus on relative sales and the difference in customer reviews, Chevalier and Mayzlin(2006) found that both the average online user review rating and the number of onlinereviews have a significant influence on book sales. They also found that negativereviews (one-star reviews) have a greater impact on book sales than positive reviews(five-star reviews). Clemons   et al.   (2006) demonstrated that the quantity of customerreviews is a good predictor for beer sales, while the average rating is not. Ye  et al. (2009)

extended their research into the hotel business and found that the average onlinecustomer review rating and the variance of the ratings both influence hotel bookings.

Most of these studies assume that the online user review is the precursor of sales,however, a recent study conducted by Duan  et al.  (2008) questioned this proposition.They used a dynamic simultaneous equation model and assumed that the onlinecustomer reviews were both the precursor to and the outcome of retail sales. Theirresults identified a positive feedback relationship between online user reviews andmovie box office revenue: the movie’s box office revenue and online customer review

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valence significantly influenced WOM volume, and the volume of online reviews inturn led to higher box office performance.

Online user reviews and search goods

According to Chen and Xie (2008), online customer reviews about high technologyproducts are likely to be more relevant to customers than seller-created information.Seller-created information is more product-oriented, and often includes the product’stechnical specifications. For example, seller-created information about a digital cameravery likely includes the optical zoom number, the LCD screen size and the number of pixels. Although these product details are important for the customers to make theirpurchase decision, they are not enough. Sometimes, the customers are also interested inhow other customers feel about the technological specifications and their conclusionsabout the camera based on the specifications (for example, whether the product is userfriendly or not, and whether the product is reliable or not). This kind of information canbe found in online reviews provided by consumers who are knowledgeable about theproduct category or have used such products. In addition, consumers who lackexpertise with the products might have difficulty understanding the benefits fromproduct attribute specification. Some online customer reviews will definitely help thoseunsophisticated customers. Based on the above arguments, we expect that online userreviews will have a positive influence on sales of search goods.

According to Huang et al. (2009), consumers tend to collect more search attributes likeprice and product specifications when purchasing search goods. We therefore expect thatproduct specifications also affect the sales of search goods. Considering that any productcan be described as existing along a continuum from pure search goods to pureexperience goods, we chose the digital camera for this research because it represents thepure search goods end of the continuum (Mudambi and Schuff, 2010) and has the keyproduct attributes of optical zoom number, pixel resolution, and display size.

MethodologyStudy 1 Data. Our research is based on the data collected from amazon.com, the biggest onlineretail store in the USA. Amazon.com provides the sales rankings of all new point andshoot digital cameras periodically, and these rankings will be used in the study. In thisresearch, the first data collection was on October 17, 2009. All web pages weredownloaded into a folder using a crawler. The data for all cameras were categorizedinto a spreadsheet and include the sales ranking for each new point and shoot digitalcamera, the price, the optical zoom number, the number of pixels, the LCD screen size,and the online customer reviews.

Amazon.com provides a five-star system for customer reviews, with five stars

representing the best evaluation and one star indicating the worst evaluation. Inaddition, it also orders reviews according to two additional priorities: the most helpfulreviews (based on how many consumers agree with the review) and the most recentreviews (based on when the review was posted on the website). The recorded review datais divided into three parts: the overall review (including the number of existing reviewsand the distribution according to the number of stars assigned by the reviewer), the mostrecent reviews (similar statistics for the most recent twenty reviews) and the most helpfulreviews (similar statistics for the 20 most helpful reviews).

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experience goods’ sales was inconsistent in previous studies. For example, based onpanel data of movie box office sales, Duan  et al.  (2008) found that the total number of online user reviews influenced the box office sales, while the average rating did not.However, according to Chevalier and Mayzlin (2006), both the total number of online

user reviews and the average user review affected book sales. Their results alsoindicated that one-star reviews carried more weight and credibility than five-starreviews. Therefore, our original model included all these online review-relatedvariables: the total review number, the average review rating, the percentage of five-star reviews and the percentage of one-star reviews.

Following the equation by Chevalier and Mayzlin (2006) and the above discussion,the model we tested has the following equation:

ln   ranki ð Þ ¼ al i  þ bzi  þ cpi  þ b  P  ln   P i ð Þ þ b 0 þ b  A AveRating i  þ b 2 HiRatei 

þ b 3 LowRatei  þ b  N  ln   Numi ð Þ:   ð1Þ

In above equation, l i , zi  and  pi  represent the LCD size, the optical zoom and the numberof pixels for the camera respectively.  P i , ranki  and AveRating i  represent the price, thesales ranking and the average customer rating of the camera respectively. The variable HiRatei  represents the percentage of five-star reviews among all the reviews; while thevariable  LowRatei  represents the percentage of one-star reviews. Finally, the variable Numi  represents the total number of the online customer reviews.

 Results. SPSS was used to fit the multi-regression model. First, the data setincluding the complete camera parameters and all online reviews was used in theanalysis. The analysis showed that there is multicollinearity among the averagecustomer review (VIF ¼ 12:5), the percentage of high ratings (VIF ¼ 6:2) and thepercentage of low ratings (VIF ¼ 6:9). This is reasonable because the average rating is

a function of the high and low rating percentages. Therefore, the following analysisused either the percentage or the average rating, but not both. The results of theanalysis are summarized in the Tables I and II.

The data in Table I tell us that the model using the percentage of five-star andone-star rating reviews is significant (  F   ¼ 125:2, p , 0:01). The independent variablesof camera price (  B  ¼ 0:68,   p ¼ 0:00), pixels (  B ¼ 20:22,   p ¼ 0:00), optical zoomnumber (  B  ¼ 20:07,  p ¼ 0:00), total number of online reviews (  B  ¼ 20:21, p ¼ 0:00),and percentage of one-star reviews (  B  ¼ 0:004,  p ¼ 0:02) are all significant. However,

 B    Std err.   p-value VIF

Const. 5.35 0.32 0.00

Ln_P 0.68 0.55 0.00 1.56Pixels   20.22 0.01 0.00 2.07Zoom   20.07 0.01 0.00 1.18Lcd 0.02 0.08 0.84 2.00ln(  Numi  )   20.21 0.02 0.00 1.24Hirate   20.002 0.001 0.16 1.45Lorate 0.004 0.002 0.02 1.54

Note: Adjust R -square is 50 percent,  F   ¼ 125:2,  p , 0:01

Table I.Complete data withpercentage rating

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LCD display size (   B  ¼ 0:02,   p ¼ 0:84) and percentage of five-star reviews(  B  ¼ 20:002,   p ¼ 0:16) are insignificant. Our results show that the percentage of negative online reviews has more influence than the percentage of positive onlinereviews on product sales, and this finding was also reported in previous studies

(Chevalier and Mayzlin, 2006; Yang and Mai, 2010).The data in Table II indicates that the model using the average online user review

rating is also significant (  F   ¼ 145:9,  p , 0:01). The independent variables of cameraprice (  B  ¼ 0:68,   p ¼ 0:00), pixels (  B  ¼ 20:22,   p ¼ 0:00), optical zoom number(  B  ¼ 20:07, p ¼ 0:00), total online review number (  B  ¼ 20:21, p ¼ 0:00), and averageuser review rating (  B  ¼ 20:14, p ¼ 0:04) are all significant. However, LCD display size(  B  ¼ 20:02,  p ¼ 0:82) is insignificant.

Because the dependent variable is the logarithmic function of the sales ranking, if aproduct has a lower sequential ranking, it has more sales. In this case, if the coefficientis negative, it means that this variable has a negative influence on the product ranking,or has a positive influence on product sales. Based on the results from Table I, thenumber of pixels, the optical zoom number, the average online customer review and the

total number of online reviews are positively influencing sales: with a larger number of pixels, higher optical zoom number, more online customer reviews or higher customerratings, a higher level of sales volume should be generated. On the other hand, a higherprice or more negative online customer reviews, negatively affects the sales of thecamera. These observations are reasonable and are in line with previous studies.

Next, we used the most recent twenty customer reviews and the most helpful twentyreviews to do the same analysis. The adjusted   R -square based on the most helpfulreviews (44 percent,   p , 0:01, and   F   ¼ 76:7 when using high- and low-ratingpercentages and 46.5 percent when using the average rating) and the most recentreviews (43 percent,  p , 0:01,  F   ¼ 75:4 when using high- and low-rating percentagesand 43.3 percent when using the average rating) is smaller than the adjusted  R -squarebased on using all reviews. The coefficients of these items (based on most helpful andmost recent reviews) are similar to those based on all reviews. Therefore, in thefollowing discussion, we only focus on the data including all reviews.

Study 2  Data. Study 1 is based on a data set collected at one specific time and the results showthat the product specifications, the price, and the online user reviews all have asignificant effect on camera sales at that specific time. It would be helpful if we couldfocus on the influence of online user reviews by collecting data from two different

 B    Std err.   p-value VIF

Const. 5.88 0.28 0.00Ln_P 0.68 0.05 0.00 1.54Pixels   20.22 0.01 0.00 2.07Zoom   20.07 0.01 0.00 1.18Lcd   20.02 0.08 0.82 2.00ln(  Numi  )   20.21 0.02 0.00 1.24Averate   20.14 0.04 0.00 1.36

Note: Adjust R -square is 50 percent,  F   ¼ 145:9,  p , 0:01

Table II.Complete data with

average rating

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times. Since the product specifications for each product do not change from onesampling time to another, the influence from these specifications should be the same. If the sales ranking changes, the difference should be from the change in online userreviews and price. Therefore, if we study the difference between these two data sets, we

should be able to focus on the influence of online user reviews. Because actual salesdata are not available from amazon.com, we used sales ranking as a proxy for salesdata in both samples. The following equation illustrates the procedures:

 y t 2ð Þ 2 y t 1ð Þ ¼  f xð Þ )  y t 2ð Þ ¼  y t 1ð Þ þ f xð Þ:   ð2Þ

The dynamic analysis based on several different data sets has been widelyimplemented to study the impact of online customer reviews (Chevalier and Mayzlin,2006; Duan   et al., 2008). For example, Chevalier and Mayzlin (2006) used theinformation from two different websites to analyze the influence of online user reviewson book sales; Duan  et al.  (2008) sampled the data from different times, and used thedifferential information to investigate the impact of online user reviews on movie

revenue.We collected the data for the second study on May 25, 2010 and June 21, 2010. Eachdata set includes the digital cameras’ product details (LCD display size, number of pixels, and optical zoom size), and its price, sales ranking, and online user rating(complete data). We did not collect the most helpful and the most recent online userreview data for this study. Similar to study 1, we only collected the listed items withcameras only and the products sold by featured sellers. We intentionally separated thetwo data collection points by four weeks. First, these two data collection points cannotbe too close to each other; otherwise, there would not be much change in online userreviews. Second, these two collection points cannot be too far apart, because digitalcameras are updated very quickly and these products do not have a long life span. If we waited too long before we collected the second data set, it is possible that a number

of the products in the first data set would be missing in the second data set. After wecollected these two sets of data, we cleaned the data by removing any products thatonly appeared in one data set, and kept only those products that appeared in both datasets. After this process, our final data set includes 428 products; the average onlinecustomer review of these products dropped by 0.02 ( þ /20.28), and total online reviewcounts increased by 0.68 ( þ /25.42).

 Empirical model . Consider two sampling points of interest (t1 and t2), and use theequation from the previous section (we used the model with the average online userrating, and ignored the LCD display size because it was not significant in study 1):

ln   ranki  _ t 1

¼ bzi  þ cpi  þ b  P  ln   P i  _ t 1

þ b 0_ t 1 þ b  A AveRating i  _ t 1

þ b  N  ln   Numi  _ t 1

  ð3Þ

ln   ranki  _ t 2

¼ bzi  þ cpi  þ b  P  ln   P i  _ t 2

þ b 0_ t 2 þ b  A AveRating i  _ t 2

þ b  N  ln   Numi  _ t 2

  ð4Þ

Using the arguments from the previous section, we can state that the sales ranking int2 is a function of the ranking in t1, and the difference of lnð P Þ, AveRating and Num.

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ln   ranki  _ t 2

¼ b  R  ln   ranki  _ t 1

þ Db þ b  P   ln   P i  _ t 2

2 ln   P i  _ t 1

þ b  A   AveRating i  _ t 2 2 AveRating i  _ t 1

þ b  N   ln   Numi  _ t 2

2 ln   Numi  _ t 1

:

  ð5Þ

In addition, we also included the price, the average online review and the number of online reviews from t1 as inputs. This is reasonable because a price change in t2 canimpact sales. In addition, the original price level also influences the change in salesbetween two sampling periods. Specifically, the same change in price will lead to asmaller change in sales for a product with a high original price than for a lower-pricedproduct. Besides, assume there are two similar products: A maintains its price, but theprice for B drops from a higher price down to the same price as A. The principle of crosselasticity tells us that this change will likely increase the sales of B relative to the sales of A. Similar arguments can also apply to online customer reviews. For example, if thereare two products A and B with the same average online review, but A’s reviewexperiences a negative shift from the previous period to the current period while Bexperiences a positive shift. Even though the existing average online user review of A isidentical to B’s, it is likely that B’s current sales are higher than that of A. Therefore, wealso included the price, the average online review and the number of online reviews fromt1 as inputs. Combining the above discussions, we come up with the following equation:

ln   ranki  _ t 2

¼ b  R  ln   ranki  _ t 1

þ Db þ b  P   ln   P i  _ t 2

2 ln   P i  _ t 1

þ b  A   AveRating i  _ t 2 2 AveRating i  _ t 1

þ b  N   ln   Numi  _ t 2

2 ln   Numi  _ t 1

þ bD zi  þ cD pi  þ d D ln   P i  _ t 1

þ b 1D AveRating i  _ t 1 þ b 4D ln   Numi  _ t 1

:   ð6Þ

This equation also adds back the number of pixels and the optical zoom number fortesting. According to the previous discussion, we expect their coefficients to beinsignificant. Following the same procedure, we can create a dynamic model with thehigh- and low-rate review percentages.

 Result . SPSS is used to fit the multi-regression model. And the results can be foundin Tables III-V. Table III is based on equation 5; Table IV is based on equation 6 andTable V is based on the model using high- and low-rate review percentages:

 B    Std err.   p-value VIF

Const. 0.48 0.10 0.00Pre_sale 0.92 0.02 0.00 1.04Diff_rating   20.1 0.05 0.10 1.03Diff_ln(  price ) 0.49 0.16 0.00 1.02Diff_ln(  Numi  )   20.2 0.05 0.00 1.09

Note: Adjust R -square is 86.8 percent,  F   ¼ 701:1,  p , 0:00

Table III.Complete data with

average rating

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Table III tells us that the model in Equation 5 is significant (adjusted   R 2 ¼ 86:8percent,   F   ¼ 701:1,   p ¼ 0:000) and the change in user reviews (  B  ¼ 20:1 and

p ¼ 0:

10), the price change (  B  ¼ 0:

49 and p ¼ 0:

00) and the change of total online userreviews (  B  ¼ 20:2 and p ¼ 0:00) are all significant. As we predicted, the coefficient of sales from the previous period is close to unit (  B ¼ 0:92 and  p ¼ 0:00).

Table IV indicates that the model in Equation 6 is also significant (adjusted  R 2 ¼

87:5 percent,   F   ¼ 300:6,   p ¼ 0:00). The change in user reviews (  B  ¼ 20:12 andp ¼ 0.08), the price change (  B  ¼ 0:42 and p ¼ 0:01) and the change in total online userreviews (  B  ¼ 20:1 and  p ¼ 0:08) are all significant. The coefficient of sales from theprevious period is close to unit (B ¼ 0:93 and  p ¼ 0:00). The above coefficients aresimilar to what we have in Table III. In addition, other significant independentvariables include: the previous price (  B  ¼ 20:07,   p ¼ 0:07) and previous onlinecustomer review count (  B  ¼ 0:04,  p ¼ 0:00). Average online review from the previousperiod is insignificant (  p ¼ 0:66). The product related features are not significant

(  p ¼ 0:

24 for number of pixels and   p ¼ 0:

54 for optical zoom number) and this isconsistent with our expectation.

Table V tells us that the model focusing on low and high-rate percentages is alsosignificant (adjusted R 2 ¼ 86:7 percent, F  ¼ 556:6 and p ¼ 0:000). However, only theprice change (  B  ¼0.49 and   p ¼ 0:00), the change in total online user reviews(  B  ¼ 20:19 and p ¼ 0:00) and sales from the previous period (  B  ¼0.92 and  p ¼ 0:00)are significant. The change of high-rate percentage (  B  ¼ 0:0 and   p ¼ 0:73) andlow-rate percentage (  B  ¼ 0:0 and p ¼ 0:89) are not significant. It is worth mentioning

 B    Std err.   p-value VIF

Const. 0.73 0.22 0.00Pre_sale 0.93 0.02 0.00 1.13

Diff_rating   20.12 0.06 0.08 1.22Diff_ln(  price ) 0.42 0.16 0.01 1.06Diff_ln(  Numi  )   20.10 0.06 0.08 1.57pre_rating   20.02 0.04 0.66 1.35ln(  pre _  price )   20.07 0.04 0.07 1.33ln(  pre _  Numi  ) 0.04 0.01 0.00 1.71Pixels   20.02 0.01 0.24 1.66Zoom 0.003 0.005 0.54 1.31

Note: Adjust R -square is 87.5 percent, F   ¼ 300:6,  p , 0:00

Table IV.dynamic model based onequation 6 (with averagerating)

 B    Std err.   p-value VIF

Const. 0.48 0.10 0.00Pre_sale 0.92 0.02 0.00 1.07ln(  price   0.49 0.16 0.00 1.03Diff_ln(  Numi  )   20.19 0.05 0.00 1.08Diff_percent_5 0.0 0.00 0.73 1.04Diff_percent_1 0.0 0.00 0.89 1.03

Note: Adjust R -square is 86.7 percent, F   ¼ 556:6,  p , 0:00

Table V.Complete data withaverage rating

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that these two coefficients are zero, and this may indicate that there is not enoughchange in these two variables. Since the changes of high- and low-rate percentages arenot significant, there is no need to add more independent variables from the staticmodel for analysis.

The results in this section show that a higher sales ranking from the previousperiod, a larger increase in average online review, a larger increase in the number of online reviews, and a larger decrease in price will all improve the sales of the product.However, Table IV also shows that a higher price and a lower total online review countfrom the previous period also improve the sales in the current period. It looks like thereis a contradiction from the results in Tables I and II, where a higher price and lowertotal online review count are associated with lower sales. After further consideration,we conclude that these two observations are actually consistent: if a product has ahigher price and fewer online reviews, it normally means that the sales of the productin this period are not good. However, if the retailer can decrease the price or boost theonline review count in the future, it has more potential to improve the sales in thefuture.

There is another interesting finding: in the previous section using the static model,the average user review is significant in influencing the product’s sales. However, inthe dynamic model, the recent change in user reviews is significant while the averageuser review from the previous period is not significant. This may indicate that theoriginal average online review rating is similar to the product related properties, and isessentially a fixed variable.

DiscussionThese two studies used the sales ranking of digital cameras to investigate the influenceof online customer reviews on search goods sales and showed some important findings.

First, the product’s properties and its listing price affect the sales of search goods.

According to Huang   et al.   (2009), consumers tend to collect more search attributeswhen purchasing search goods, and the product specifications and price play a role inaffecting customer’s purchase decision. Huang  et al.  (2009) therefore pointed out thatvendors of search goods may benefit from reducing the price, and this is probably notvalid for experience goods. Our finding confirmed this from two aspects: the sales of one specific model are related to its price; when the price drops, the sales of thatproduct improve.

Second, our study clarifies an uncertainty about the influence of online user reviewson search goods: online customer reviews affect the sales of search goods. Generally,both the average online customer review ratings and the count of total online userreviews influence the sales of search goods. This result is not exactly the same as theresults from experience goods research. Even though it has been widely agreed that

online user reviews help sales of experience goods, past research cannot agree onwhich aspect of online reviews matters. For example, some research shows that theaverage rating is important (Chevalier and Mayzlin, 2006), while others find that onlythe total number of reviews helps (Clemons  et al., 2006). The different findings betweenour study and the literature studying experience goods are probably related to theinherently distinct natures of search goods and experience goods. According toMudambi and Schuff (2010), reviews of search goods are more likely to addressobjective aspects of the product, and both moderate and extreme reviews are credible;

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while reviews of experience goods normally carry subjective sentiments, extremelypositive and negative reviews are less helpful. As a result, all online reviews of searchgoods count, and both the number and the average rating influence purchase decisionsand sales. On the other hand, not all the online reviews of experience goods are

important; research on different websites and products may yield different results.Third, our study also shows that negative one-star reviews have more impact than

positive five-star reviews on sales. This asymmetric impact of one-star reviews andfive-star reviews can be interpreted in several aspects. First, a negative performance onan attribute generally has a greater effect on purchase intentions than a positiveperformance (Mittal   et al., 1998). Considering that a larger percentage of one-starreviews reflect the more negative aspects of the product performance, one-star(negative) reviews should generate a more significant impact on future sales than thefive-star (positive) reviews. Second, the sellers may post some fake positive reviews topromote the sales of their products. As a result, one-star reviews carry more credibilityand therefore can have more influence than five-star review does (Chevalier andMayzlin, 2006).

With previous sales as an independent variable, the product related properties andaverage online review rating are not significant any more. Instead, the change in theaverage online review rating, the change in the total online review count and thechange of price are significant predictors. The second study also revealed aninteresting finding: when we focus on a specific period, lower price and more onlinereviews are positively associated with product sales. However, when we focus on adynamic transition, lowering the price and increasing the number of online reviewspositively influence product sales, and surprisingly, a higher price and less onlinereviews in a previous period also boost sales in the current period. This interestingfinding may indicate a useful marketing strategy: during an early period of a product’srelease, the manufacturer can limit the available product quantity and sell the product

at a higher price. When necessary, the manufacturer can reduce the price to greatlyboost the sales in the following period.

Summary and limitationTo the best of our knowledge, this research is one of the first attempts to connect theonline consumer-generated reviews and the sales of search goods (digital cameras inthis research) and it has identified several significant findings.

Our research clearly reveals that there is a significant relationship between theonline user review and sales of search goods. This finding is important for both firmsand academics. It indicates that the retailers should provide channels for andencourage customer online reviews for search goods to improve sales. It also suggeststhe influence of online user reviews on search goods sales is different from that on

experience goods. For example, we find that negative reviews are more important thanpositive reviews for search goods, while this is not consistently reported on experiencegoods. Similarly, the average rating of reviews is significant in predicting sales of search goods, while some research on experience goods found otherwise. Thesedifferences might be attributed to the subjective nature of experience goods and theobjective nature of search goods. Future research can conduct a more content-basedinvestigation to find out whether this objective nature originates more from theproduct or the reviews written about it. In other words, if a group of subjective online

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reviews and a group of objective reviews are associated with the same experiencegoods or search goods, will they have different impacts on customers’ purchasedecisions?

Our study also recognizes that the product specifications influence the sales of 

search goods. Therefore, it is beneficial for online retailers to provide detailed productattributes to help their customers make the purchase decision. In addition, our researchon search goods shows that price at the specific time and price changes are significantfactors affecting sales. Therefore, carefully designed and executed price promotionscould be effective ways to improve sales of searchable goods. Again, this pricesensitivity might be much less for experience goods. Future research can explore thisdifference with data from both experience and search goods.

Due to limited resources, this study is based on one specific type of search good,future research may want to focus on additional types of search goods. Because allproducts can be described along a continuum from pure search goods to pureexperience goods, it would be helpful if future research can cover more products at

various points on this spectrum and test the generalizability of the results of thisresearch.Our dynamic model is based on only two sampling points. If additional samples are

collected, we could investigate whether and how the time delay between these samplesinfluences the sales ranking prediction. More data may even enable us to track howonline customer reviews influence product sales throughout the life cycle of searchproducts: for example, how sales are affected and how online user reviews aregenerated when there are no existing online user reviews associated with a product.

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About the authorsLin Zhang is an Assistant Professor of Marketing at Truman State University. Her research

interests include brand management, comparative advertising and unhealthy consumptionbehavior.

Baolong Ma is an Associate Professor of Marketing at Beijing Institute of Technology and aprimary researcher of The Retailing Research Center at Tsinghua University. He worksprimarily in the areas of customer relationship management, brand management and crisismanagement and has written numerous scholarly articles on topics in these areas. Baolong Ma isthe corresponding author and can be contacted at: mabaolong@gmail.com

Debra K. Cartwright is a Professor of Marketing at Truman State University. Her researchinterests include strategic responses to external environmental pressures, competitive businessstrategies, assessment of student learning, brand management and services marketing.

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