Harnessing the Influence of Social Proof in Online Shopping: The Effect of Electronic Word of Mouth on Sales of Digital Microproducts

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1 Harnessing the Influence of Social Proof in Online Shopping: The Effect of Electronic Word of Mouth on Sales of Digital Microproducts Naveen Amblee and Tung Bui ABSTRACT: Social commerce has taken the e tailing world by storm. Business-to-consumer sites and, more important, intermediaries that facilitate shopping experience, continue to offer more and more innovative technologies to support social interaction among likeminded community members or friends who share the same shopping interests. Among these technologies, reviews, ratings, and recommendation systems have become some of the most popular social shopping platforms due to their ease of use and simplicity in sharing buying experience and aggregating evaluations. This paper studies the effect of electronic word of mouth (ewom) communication among a closed community of book readers. We studied the entire market of Amazon Shorts e books, which are digital microproducts sold at a low and uniform price. With the minimal role of price in the buying decision, social discussion via ewom becomes a collective signal of reputation, and ultimately a significant demand driver. Our empirical study suggests that ewom can be used to convey the reputation of the product (e.g., the book), the reputation of the brand (i.e., the author), and the reputation of complementary goods (e.g., books in the same category). Until newer social shopping technologies gain acceptance, ewom technologies should be considered by both e tailers and shoppers as the first and perhaps primary source of social buying experience. KEY WORDS AND PHRASES: Digital microproducts, digital products, electronic word of mouth, ewom, social proofs. Social commerce has taken a predominant role in the e commerce world. Buyers have migrated from the in-store shopping experience to online shopping engagement through a variety of means ranging from friends recommendations, customer reviews, and ratings to how-to guides via Web 2.0 platforms. Signals from trusted sources are known to be most useful and effective for products that a shopper has yet to experience [26]. One increasingly important source of signals is social proof, whereby consumers rely on the collaboratively shared information and experiences of others to infer a course of action [32, 35, 36]. With annual sales of more than $34.2 billion in 2010, Amazon.com is the leading e commerce site and continuously finds ways to nurture thriving virtual communities to form opinions, share experiences with circles of friends, and provide highly trusted recommendations [25]. It has designed a formalized and structured form of social customer relationship management that allows individuals and groups of people with a shared business interest buyers and sellers to actively engage in e commerce decisions. The authors thank Professor Reginald Worthley for his statistical insights into the research findings, the three anonymous reviewers, and the guest editors for their valuable suggestions. International Journal of Electronic Commerce / Winter , Vol. 16, No. 2, pp Copyright 2012 M.E. Sharpe, Inc. All rights reserved /2012 $ DOI /JEC

2 92 Amblee and Bui Although there are many forms of social interaction, the most commonly adopted form is online ratings and reviews. Word-of-mouth communications have been shown to influence awareness, expectations, perceptions, attitudes, behavioral intentions, and behavior [19]. They can be either positive or negative, and there is a strong incentive for consumers to gain something for nothing by reading reviews from others who share the same interest in order to help make a decision [3, 20]. Reviews and ratings can be written or read by a consumer who is unknown to others, an adviser or expert, or a close and trusted friend. As discussed below, the roles played by these three sources are different in nature. This research seeks to measure the effect of reputation signals generated by electronic word of mouth (ewom). To help focus on the nature and importance of ewom, we studied the market for digital microproducts. Unlike many typical products, digital microproducts often have a selling price that is very low (sometimes they are even free), fixed, and identical for all products, and there are no delivery costs. Examples of digital microproducts include digital music tracks, apps for mobile phones, and short e books. Since the role of price on buying decision is no longer a key factor, quality perception is likely channeled through social interaction in the form of ewom. We conducted a year-long empirical study of Amazon Shorts, a digital microproduct that contains a short story or guide and is available from Amazon.com. 1 Book lovers tend to join book clubs to get recommendations, engage in content discussions, ask for reflection on a character in a book, and so on. ewom on e books is a simple and convenient means of communicating and sharing experience for a socially engaged, well-read community of readers. Prior research has focused on ewom as a signal of product quality, a dimension of product reputation. In this study, we extend existing research to include the effect of ewom as a signal of brand reputation as well as a signal of the pooled reputation of complementary goods. We study ewom as a means of information exchange, thought sharing, conversation, and recommendation among book lovers regarding the quality (i.e., the reputation) of books and authors. Social Influence, Social Commerce, and ewom Social commerce involves the use of Web 2.0 technologies such as social networks and user-generated content to assist in the acquisition of products and services [14]. The concept of social commerce evokes the notion of a network of consumers with very strong ties (e.g., trusted friends), made possible recently with the widespread adoption of online social networks such as Facebook, Twitter, Google+, and Foursquare, to name a few. Tie strength is a measure of the quality and strength of social relationships [7], and ewom communications via online customer reviews act as routes for social influence [33]. Social influence is the process by which individuals make changes to their thoughts, feelings, attitudes, or behaviors as a result of interaction with individuals or groups who are perceived to be similar or desirable or with experts who are

3 International journal of electronic commerce 93 recognized by the community of buyers as knowledgeable about the product. Thus, while information gathering is a primary motive to get informed about the product, there is a considerable element of social interaction involved in terms of getting empathy and intimate discussion between well-meaning friends. It has been shown that book readers, as consumers, articulate themselves through online reviews and conversation (ewom) because they are strongly motivated by their concern for others and by the potential to enhance their own self-worth vis-à-vis their friends [13, 20, 24]. Since friends alternately act as information seekers and information providers, social interaction is likely sustained over time through continued discussion threads. Online recommendation sources can be sorted into three categories, namely, regular consumers, human experts, and expert systems such as recommender systems. It is likely that the shopper looks at reviews and ratings from unknown customers or experts as a source of accurate and unbiased information regarding a particular product. In addition to needing information about the product, the shopper will seek reviews from friends as a source of emotional, possibly nonjudgmental guidance and support a personal touch in the buying decision process. ewom communications by experts have the potential to provide professional advice with a certain level of authority, whereas ewom and feedback between friends offer opportunities for conversation at the level of trust and friendship. Online automatic recommendation systems tend to have more influence on consumer choices than human experts or other anonymous consumers [41], but they may be biased by commercial motives [40]. ewom as a Socially Generated Signal of Product Reputation With the emergence of social commerce, there has been renewed interest in online feedback mechanisms that allow for storing and exchanging ewom, which are referred to as reputation systems [13, 38]. Reputation systems allow buyers to socially infer reputation by observing and discussing the ratings of a product by others (ewom). At the same time, indirect sources of product reputation include group-derived reputations, which involve reputation developed via the perception of a group to which the individual product belongs [31]. Reputation is the estimation of the consistency of a product or brand and is established by fulfilling marketing signals [21]. A market signal is an activity that provides information regarding the product beyond mere form, such as information on product quality and other unobservable aspects [21]. ewom is an important market signal of reputation, and ratings are used extensively to convey either positive, neutral, or negative reputations [37]. Consumers who know each other often share experience through online feedback mechanisms, or reputation systems [13]. Thus, structured ewom is a source of social capital and helps communicate product knowledge. In this role, ewom can both predict sales as well as cause a change in sales by influencing consumers [10, 25, 29].

4 94 Amblee and Bui ewom as a Socially Generated Signal of Brand Reputation In this paper, we look at direct reputation via reviews of a product as well as indirect group-derived reputation via the brand reputation and the reputation of complementary goods [1, 31]. Word of mouth plays an important role in consumers perception of a particular brand name. Consumers who are particularly pleased or displeased with a brand will make their opinions known to other consumers through word-of-mouth communication, hoping their friends and others may benefit from their experience [39]. Positive word of mouth has been shown to lead to favorable attitudes toward a particular brand [22]. Conversely, when consumers share their negative experiences (dissonance reduction) and react by switching brands [39], they likely will discourage their friends from purchasing the product. Online brand trust, which is the willingness of consumers to rely on the ability of the brand to perform its stated function, is strongly influenced by word of mouth [19], and high brand trust leads to higher sales [8]. Research has shown that feedback from reviews can affect brand reputation [12]. Since products within a particular brand contribute to and share its brand reputation, and constitute its brand portfolio [30], it is very likely that ewom related to the products in the portfolio will have a significant impact on a product purchasing decision. Familiarity with products from the same source has been shown to have a positive effect on trust and loyalty [34]. It is safe to argue that ewom generated for a brand by means of reviews of the products in a portfolio can be used as a proxy of the brand s reputation and as such will be correlated to that product s sales [1]. Classical economics models assume that consumers expect perfect information about a product, but research has shown that, in reality, consumers will perform a product information search but will stop short of becoming perfectly informed, due to the rising cost of the information search [4, 33, 44]. Often, consumers will partially or wholly substitute brand information for product information, especially when they are not familiar with the good [33, 47]. Thus, consumers will likely use a mix of brand and product information while making a purchasing decision for experience goods such as digital microproducts since it is difficult to judge quality prior to consumption. Because we theorize that ewom regarding the brand s portfolio can signal the brand s reputation, such ewom should be able to substitute for product ewom when the latter is unavailable. We test this interaction of brand and product information search for digital microproducts in the context of ewom (see Figure 1). Shoppers tend to focus attention on the product first. When discussion of the product (e.g., a particular book) is not available, they will seek information on the brand (e.g., the author of the book). ewom as a Socially Generated Reputation of Complementary Goods Individual product reputations, as signaled by ewom, can be bundled together into a single numerical rating 2 to represent the reputation of complementary

5 International journal of electronic commerce 95 Figure 1. Product Reviews, Brand Reputation, and Effect on Sales goods [1]. This is the second type of group-derived indirect reputation. We extend our analysis to look at the effect of the reputation of complementary goods, and we hypothesize that the average ewom for the items in the recommendation pool will lend its reputation to the product being reviewed, and as such be positively correlated to sales of the product. If the reputation of the complementary goods is poor, it is expected to be reflected in poorer sales of the product, and conversely, a strong reputation of complementary goods will result in higher sales of the product. An Empirical Study: The Effect of ewom on Sales of Amazon Shorts In this research we analyze the impact of ewom on Amazon Shorts. Shorts are electronic books, or e books, available for download in PDF format from Amazon.com. Each Amazon Short consists of a short story or other literary work, thus the name Short. Shorts became available in August 2005 and are featured quite prominently on Amazon.com. They are priced at a flat rate of 49 cents each and can be classified as digital microproduct. Initially, 65 Shorts were made available, with a steady stream of additions over time. As of the end of September 2007, there were over 2,000 Shorts available for purchase. 3 Digital microproducts are experience goods with almost insignificant price elasticity (low and fixed price), and thus the motivation to read online WOM for digital microproducts is expected to be even more compelling, which in turn is expected to influence the purchasing decision. In a market of highly specialized and homogeneous products, such as leisure books purchased from a book club, the distinction between advice from experts and friends could be blurred. We are unaware of any research conducted on the impact of ewom on the sales of digital microproducts, yet buying decisions seem to increasingly rely on social-based signals.

6 96 Amblee and Bui Research on the impact of ewom on product sales has focused on two main attributes, namely, volume (number of messages that friends sent to each other), and valence (nature of the rating or message [review], which can be positive, negative, mixed, or neutral) [29]. Chevalier and Mayzlin [10] found that differences in the ratings (valence) for the same book are responsible for the difference in relative sales rank. However, Chen et al. [9] found no correlation between review valence and sales. Duan et al. [15] found that the volume of ewom is predictive of box-office sales, but the valence has no explanatory power. The majority of past research suggests that the valence of ewom is not a reliable predictor of sales, but the volume is. A partial explanation is that herding behavior for the most popular products obscures the impact of ewom for these products [16]. Research Hypotheses Based on the preceding theoretical discussion, we propose the following hypotheses for testing the effect on product sales of ewom as a social signal of reputation. 4 Hypothesis 1: Effect of social commerce on product reputation and sales. H1a: The valence of customer reviews for a digital microproduct will have a positive correlation with sales of that digital microproduct. H1b: Digital microproducts with customer review(s) will have better sales than those digital microproducts without any reviews. H1c: The volume of customer reviews for a digital microproduct will have a positive correlation with the sales of that digital microproduct. Hypothesis 2: Effect of social commerce on brand reputation and sales. H2a: The brand reputation of a digital microproduct, as signaled by ewom, will be positively correlated to sales of the digital microproducts. H2b: When customer reviews (ewom) for the digital microproduct do not exist, the brand reputation, as signaled by ewom, will have a higher positive correlation with sales than when customer reviews do exist (see Figure 1). H2c: When customer reviews for the digital microproduct do exist, the brand reputation, as signaled by ewom, will have a lower positive correlation with sales than when customer reviews do not exist. Hypothesis 3: Effect of social commerce on reputation of complementary goods and on sales.

7 International journal of electronic commerce 97 H3: The reputation of complementary goods of a digital microproduct, as signaled by ewom, will be positively correlated to the sales of the digital microproduct. Measures Validated Customer Ratings as Socially Generated Product Reputation Amazon.com provides a customer-driven platform to promote knowledge exchange from the community of readers. Readers are encouraged to post online reviews, in the form of text, as well as a numerical rating from 1 to 5. An average customer rating score is provided for all Shorts with reviews. This rating also ranges from 1 to 5, comprising the average of all readers ratings for that particular Short. Amazon s average customer rating score has been validated as a measure in previous research on ewom [10]. Author Ratings as Socially Generated Brand Reputation For an e book, the reputation of the author who wrote the book can be regarded as its brand reputation. However, Amazon.com does not provide a score to measure the brand reputation (brand rating) of a Short. For each Amazon Short, we use the author rating (same as brand rating) score, which was developed in previous research [1]. For each of the authors, we obtained a list of up to ten of the author s works and then averaged out the average customer rating for each of those works. This score is considered the author rating of a particular Short. The resulting scores range from 0 to 5, like the average customer rating for a product. Similar Products Rating as Socially Generated Complementary Goods Reputation For each Short (or any other product) on Amazon.com, a list of similar products is included on the same Web page. This list consists of products that Amazon.com considers to be complementary (or perhaps even supplementary) to each Short being browsed, and they are displayed on the basis that customers who bought this item also bought [these items]. As with the case of the brand rating, there is no direct measure made available on Amazon.com to quantify the reputation of complementary goods. Therefore, we used a previously developed similar products rating (or SP rating) score for each book [1]. This score is calculated by first obtaining a list of all similar products for the Short in question and then averaging out the average customer rating for each of those products. Like the author rating, the SP rating ranges from 0 to 5.

8 98 Amblee and Bui Sales Rank as Proxy for Sales Amazon.com does not provide the actual sales numbers for Amazon Shorts. We used instead the sales rank of the Short as a proxy of actual sales. The sales rank is available on request from Amazon Web Services. It has been used previously as a proxy for sales [5, 6, 9, 10, 23] and is inversely related to sales; the lower the sales rank, the higher the sales. Longitudinal Study We collected data daily for 56 weeks. Shorts were introduced at an exponential pace, 5 with fewer than 150 Shorts introduced in the first six months and several hundred more Shorts were introduced over the following year. As of February 10, 2007, we collected daily data on 1,004 Shorts, 133 of which were used in this study. These comprise the earliest batch of Shorts to be released. One of the most significant problems in measuring ewom is the endogeneity problem [18, 36]. Word of mouth is inherently endogenous in nature, that is, it is both a cause and outcome of sales. Controlling for this dual nature of ewom is a challenge. To conduct our longitudinal study, we tested our demand model first for one single day s results and then repeated the analysis over a 56-week period to ensure that the findings held consistently and were not affected by sudden changes in the sales rank of the books, given that the sales ranking applies to all items listed on Amazon.com, which number in the millions. This process allowed us to monitor the consistency and incremental impacts of book readers discussion over time. Results Summary Statistics The hypotheses and summary statistics were calculated weekly. For the sake of brevity and practicality, changes are reported graphically. We picked May 15, 2006, as the halfway point in the observation period, a common practice in time series analysis. Summary statistics for all the variables are shown in Tables 1 and 2. Although the mean sales rank for the Amazon Shorts was about 758,000, the range is quite wide. The short with the lowest sales (and consequently highest sales rank) had a sales rank of 2.42 million, and the short with the highest sales had a sales rank of just 6,837, meaning that it was the 6,837th most popular product sold on all of Amazon.com. The average customer rating of each online review was 4.42 out of 5, which is extremely skewed. Although the lowest possible rating was 1 out of 5, the lowest ever provided was 2, and even this was given very rarely. This could be explained by the fact that Amazon editors prescreened the Shorts that were made available. The average number/volume of reviews was 1.82, and

9 International journal of electronic commerce 99 Table 1. Descriptive Statistics. N Minimum Maximum Mean Standard Deviation All Shorts Sales rank 133 6,837 2,423, , , Average customer rating Number of reviews Author rating Average SP rating Shorts with Reviews Sales rank ,064, , , Average customer rating Number of reviews Author rating Average SP rating Shorts Without Reviews Sales rank , ,423,160 1,061, , Average customer rating Number of reviews Author rating Average SP rating for Shorts with reviews the average rose to Over a one-year period, the number of Shorts with reviews rose from 70 to 92 at the end of this period (Figure 2a). The average volume of reviews rose from about 1.25 to just over 2 reviews per Short (Figure 2b). The author rating averaged about 2.88 out of 5 for all Shorts (Figure 3a), although Shorts with reviews had a higher author rating (3.16) than Shorts without reviews (2.28). However, both the range and standard deviation were similar. The author rating was remarkably consistent throughout the year. The similar products rating 6 averaged about 3.11 for all Shorts (see Figure 3b). Like the author rating, Shorts with reviews had a higher average SP rating (3.51) than Shorts without reviews (2.28). The ranges were identical, and the standard deviations were similar. The SP rating increased in a nonlinear fashion from an average of about 2 to about 3 by the end of the year. As shown in Table 2, the correlations are all within acceptable levels (< 0.50). The average customer rating was not included in this table because Shorts without reviews (about a third of all Shorts in our study) do not have ratings assigned to them. We did not give Shorts without reviews an average customer rating of zero, since that would have implied that these Shorts were poorly rated, which was not the case. 7 Table 2 also shows the correlations for only those Shorts with reviews, with the average customer rating included. They dropped considerably.

10 100 Amblee and Bui Table 2. Correlations. Average customer rating Number of reviews Author rating Average SP rating Correlations for All Shorts (n = 133) Number of reviews Pearson correlation ** 0.290** Sig. (two-tailed) Author rating Pearson correlation 0.308** ** Sig. (two-tailed) Average SP rating Pearson correlation 0.290** 0.430** 1 Sig. (two-tailed) Correlations for Shorts with Review(s) (n = 91) Average customer rating Pearson correlation * Sig. (two-tailed) Number of reviews Pearson correlation 0.216* Sig. (two-tailed) Author rating Pearson correlation ** Sig. (two-tailed) Average SP rating Pearson correlation ** 1 Sig. (two-tailed) * Significant at the 0.05 level (two-tailed); ** significant at the 0.01 level (two-tailed). ewom and Product Reputation Hypothesis H1a (Not Supported): To help us understand the nature of social commerce and the impact of ewom as a signal of product reputation on sales, we examined the effect of the average customer rating on the sales rank: Sales Rank = α + β1 * Average Customer Rating + ε. The regression results do not show support for H1a (Table 3). Thus, there is no statistically significant correlation between the average customer rating for a digital microproduct and sales. Only those Amazon Shorts with reviews attached to them were included in the analysis. We attribute this to the fact that there is very little variability in the average customer rating score. More than 60 percent of all reviews for Amazon Shorts have a rating of 5, and ratings between 4 and 5 account for 30 percent of all reviews. This means that over 90 percent of all reviews are rated more than 4 out of 5, with only 10 percent of reviews being rated fewer than 4 stars. The average customer rating for Shorts with reviews is 4.42, and the standard deviation is This

11 International journal of electronic commerce 101 Figure 2a. Number of Shorts with Reviews (n = 133) Figure 2b. Average Number of Reviews per Short (n = 133) lack of variability likely accounts for the insignificant predictive power of the average customer rating score (i.e., valence) and is consistent with previous research [9, 15, 29]. Hypothesis H1b (Supported): To verify if there is a statistically significant difference between the mean sales rank of Shorts with no customer reviews attached to them and those Shorts with customer reviews attached to them, we conducted a t-test and obtained a t-value of 4.07, which allowed us to conclude that the two means are statistically different at the 0.01 level. Shorts with customer reviews attached to them have a lower mean sales rank, which means that they have more sales, as the sales rank is inversely related to total sales. The mean sales rank for Amazon Shorts with customer reviews is about 619,035, and the mean sales rank for Amazon Shorts without customer reviews is just over a million (1.061 million). This suggests that, on average, when an Amazon Short gets reviewed, its sales rank jumps by 442,141 rank points. We compared means for the two groups over the 56-week period and determined that the group of Shorts with reviews consistently had a lower mean sales rank than the group of Shorts without reviews. 8 Hypothesis 1c (Supported): The regression between the sales ranks and the volume of customer reviews for Amazon Shorts is statistically significant (p < 0.01):

12 102 Amblee and Bui Figure 3a. Author Rating over Time Figure 3b. Average Similar Products Rating over Time Sales Rank = α + β1 * Volume of Reviews + ε. The total volume of reviews posted for an Amazon Short can explain 15.9 percent of the variance in the sales rank. 9 With a strong F test (26.04), the standardized beta is 0.407; that is, a 1 percent increase in the volume of reviews would improve the sales ranking by percent. Alternatively, an increase of one customer review would improve the sales rank by over 100,000 rank points. When no customer review exists, the sales rank for the Short is close to 1 million. We charted the explanatory power of the volume of online customer reviews over the 56-week period. The explanatory power fluctuated considerably, 10 with a mean R 2 value of and a range between and ewom and Brand Reputation Hypothesis 2a (Supported): The regression between the sales rank and the author rating is statistically significant (p < 0.01): Sales Rank = α + β1 * Author Rating + ε.

13 International journal of electronic commerce 103 Table 3. Results of Hypothesis Testing (All Supported). H1a H1c H2a H2b H2c H3 Model Constant +380, ,648.48*** +1,367,315.2*** +1,582,130.3*** +1,025,131.8*** +1,353,657.0*** +1,562,161.8*** Average customer +53, rating (reviews) Number of reviews 106,722.4*** 62,080.26*** Author rating 211,056.5*** 87,108.80** Author rating (no reviews) Author rating (reviews) 228,495.0*** 128,406.3** Average SP rating 190,827.7*** 140,754.0*** Model fit F-value *** *** 9.218*** 6.633** *** *** Adjusted R N * p < 0.10; ** p < 0.05; *** p < 0.01.

14 104 Amblee and Bui The author rating explains 17.0 percent of the variance in the sales rank. The mean R 2 value was 0.149, and the range between and (Figure 3b), with an F value of The standardized beta score of suggesting that a one-percentage-point increase in the author rating would improve the sales rank by percent. Alternatively, a one-percentage-point increase in the author rating would improve the sales rank by over 211,000 rank points. The impact of the author rating on the sales rank was mapped over the 56-week observation period, and the results seem more stable than for the volume of reviews. The valence of ewom signaling brand reputation is able to better explain sales than the volume of ewom signaling product reputation. Hypothesis 2b (Supported): When a customer rating for the Amazon Short does not exist, the author rating has a higher correlation with sales than when a customer rating does exist: H2b: Sales Rank = α + β1 * Author Rating * Dummy2 + ε. The above regression included only those Amazon Shorts without reviews attached to them. The results (Table 3) show that the regression is statistically significant (p < 0.01) and the model F value is moderately high at When no customer review exists, the author rating is able to explain 16.7 percent of the variance in the sales rank. The standardized beta score of indicates that a one-percentage-point increase in the author rating in this case would increase the sales by percent. Alternatively, a one-percentage-point increase in the author rating for Shorts with no review would raise the sales rank by over 228,000 rank points. The 56-week analysis of the explanatory power of the author rating for those Shorts with no review shows a mean R 2 value of 0.166, with a wide range ( ). However, barring a few outliers, most values are within a much smaller range. Hypothesis 2c (Supported): When a customer rating for the Amazon Short does exist, the author rating has a lower correlation with sales than when a customer rating does not exist: Sales Rank = α + β1 * Author Rating * Dummy1 + ε. The above regression included only those Amazon Shorts with reviews attached to them. The results in Table 3 show that the regression between the author rating and the sales rank is statistically significant, but only at the 0.05 level, with an F value < When a customer review exists, the author rating would be able to explain percent of the variance in the sales rank, which is markedly lower than the 16.7 percent explained when no review exists. The standardized beta score of implies that a one-percentage-point increase in the author rating would improve the sales rank by percent. Alternatively, a one-percentage-point increase in the author rating for Shorts with reviews improves the sales rank by over 128,000 rank points. This is 100,000 rank points less than for those Shorts without reviews, clearly showing that the author rating has a much greater impact when no reviews exist. Interestingly, for Shorts with customer reviews, the correlation between the average customer rating and the author rating is essentially zero (0.006, p >

15 International journal of electronic commerce 105 Impact of Author Rating With and Without Reviews Figure 4. Author Rating 0.95). We postulate that the average customer rating and the author rating are influencing the customer in opposing directions, with the average customer rating being more dominant. The 56-week analysis of the explanatory power shows considerable fluctuation, with a mean R 2 value of and a range between and In contrast to the previous explanatory power charts, many of the lower R 2 values are not significant at the 0.05 level. A look at the explanatory values for Shorts with reviews and those with no reviews placed together gives a clearer picture (see Figure 4). Although there is considerable fluctuation in the R 2 values, the R 2 values for those Shorts without reviews is consistently and significantly higher than the R 2 value for Shorts with reviews. ewom and Reputation of Complementary Goods Hypothesis 3 (Supported): Regression results show that there is indeed a significant correlation between the average customer ratings of similar products recommended by the online recommendation system (SP rating) and sales: Sales Rank = α + β1 * Similar Items Rating + ε. The SP rating is able to explain 29.3 percent of the variance in the sales rank, with a high F value (55.64; see Table 3). The standardized beta score of suggests that a one-percentage-point increase in the average rating of complementary goods would improve the sales rank by percent. Alternatively, a one-percentage-point increase in the SP rating should raise the sales rank by over 190,000 rank points. 11 The 56-week explanatory power chart shows the R 2 trailing off after about 40 weeks, but returning again after week 50 (see Figure 4). The mean R 2 value is 0.25 and ranges between and The SP rating noticeably has the strongest impact on the sales rank. Thus, the reputation of complementary goods has the strongest correlation with sales of digital microproducts.

16 106 Amblee and Bui Finally, to measure the effect of all three reputation signals on sales of digital microproducts, we regressed the number of customer reviews (signal of product reputation), the author rating (signal of brand reputation), and the SP rating (signal of complementary goods reputation) against the sales rank, over a 56-week period. The mean R 2 value over this period was 0.33 (Table 3, under column named Model), which stands up well against similar empirical studies and ranged from 0.23 to The ability to explain, on average, a third of the variance in the sales rank is a significant contribution to the field of research on the impact of social proof on sales. Discussion All the hypotheses except H1a were supported by the data. This supports our position that ewom could be used as a socially generated reputation signal to model demand for digital microproducts. The high values of the constants for the sales rank in all the results suggest that without ewom, sales are very poor. The results convincingly show that ewom does indeed play a significant role as a signal of reputation generated by the community of readers. Customer Ratings, Reviews and Sales of Digital Microproducts Our study validates the importance of ewom to sales, as digital microproducts with reviews attached to them had significantly better sales than those products without reviews. Our analysis confirms earlier work that the volume of reviews matters and is more important than the ratings in predicting sales [15, 29]. However, the valence represented by the average of all customer ratings does not seem to play a significant role in the consumer s purchasing decision. Our study corroborates prior findings that the valence does not affect sales [15, 29]. While the binary state of reviewed/not reviewed has an impact on sales, the lack of variability among ratings within reviewed digital microproducts means that the rating itself has no impact on sales. Researchers have attributed this to the valence being overwhelmed by volume [29]. Recent research has identified information cascades or herding as an important factor behind the lack of correlation between online customer ratings and sales [16]. At first, this seems counterintuitive since the author rating and the SP rating, which are both valence measures, were significantly and positively related to sales of Amazon Shorts, while the product rating itself was not. This can be explained by the fact that while there is little variation in the average customer rating of a Short, there is significant variation in both the author rating and the SP rating (Table 1). Author s Brand Reputation The most intriguing finding of our study is the impact of the author s reputation or brand reputation on sales of Shorts. We find support for the substitut-

17 International journal of electronic commerce 107 ability of product information search and brand on e bookstores equipped with social commerce capacity. Author rating is shown to be able to predict the sales of a Short to a slightly larger extent than the volume of reviews. This is especially important when no review exists. When a mixed group of reviewed and not reviewed microproducts is taken into consideration, then author rating would have a strong impact on sales. Interestingly, when a homogeneous group of either only reviewed or only not reviewed microproducts is being considered, the impact of the author rating on sales diminishes. In the case of not-reviewed digital microproducts, the impact is only slightly reduced. For reviewed books, the impact of author rating loses its significance considerably. Arguably, the existence of customer reviews leads to consumers focusing their attention on the reviews rather than other works by the same author. Conversely, the absence of customer reviews increases the importance of the author rating, as shoppers look for other sources of product information. We believe these findings make a significant contribution to the existing body of research on ewom. Reputation of Complementary Goods We found that the reputation of complementary goods does indeed have a highly reliable and stable effect on sales, and that this impact is higher than that of the author s reputation. In other words, complementary goods lend their collective reputation to the product under consideration as being worth purchasing. The finding confirms an earlier research claiming that recommended products are twice as likely to be purchased [41]. The possibility of consumption in bundles may explain this phenomenon as well. The link with social proof is strongest with the SP rating, in that it directly conveys the purchasing actions of other consumers because it is based on the notion that customers who bought the Short also purchased the other products in the pool. The finding that the SP rating has the strongest positive correlation with sales implies that social proof has a very strong influence on sales. Implications and Recommendations for Social Commerce This research examines the influence of ewom, as a popular form of social signal of various types of reputation, on the sales of digital microproducts. We have shown that exchanged information, opinions, and recommendations from a community of book readers can be used to gauge the reputation of an e book (product reputation), the reputation of the author (brand reputation), and the reputation of complementary books, all of which affect sales. This implies that ewom, when taken globally in an online market, can be considered as a significant source of social capital capable of predicting shoppers buying decisions. The findings in this paper lead us to make several recommendations. First, we recommend that e commerce platform operators such as Amazon.com continue to encourage their community of shoppers to post reviews online since the volume of reviews is linked to increased sales [35]. A

18 108 Amblee and Bui financial incentive to those who purchased a particular digital microproduct, for example, in the form of a small credit on future sales, could dramatically increase the volume of reviews, leading to improved sales by boosting the consumer s social confidence in the experience good. Second, we propose a better scoring system than the current 1 to 5 star rating system, allowing for more variability. This could be achieved through multiple scores for writing style, content, ease of use for digital microproducts, and so on. This multiple-criteria evaluation method has already been used for reviews on video-gaming Web sites. Alternatively, we suggest that a weightedmean scoring system that takes the volume of reviews into account be used, as opposed to the current practice where all reviews are just averaged out to obtain an average customer rating. As a suggested topic for future research, we should explore whether or not readers might find a weighted-mean scoring system too cumbersome. Third, we reiterate the recommendation by Amblee and Bui [1] that platform operators such as Amazon.com develop a brand-rating score such as the one we have devised for this study. This is particularly important in the absence of customer ratings. The lack of both product information through a customer review and brand reputation through author rating could potentially discourage the shopper from buying the good. For digital microproducts other than e books, such as music and video clips, the author can be replaced with artist, actor, or director, as appropriate. Since our findings show that consumers do indeed take the customer rating of other works by the author into account, the prominent display of such a score could lead to more efficient decision making. This is particularly important for a good that has yet to have a customer rating since brand reputation can be substituted for product reputation. We recommend that the brand rating be made more prominent and available to consumers when a product rating is not available. Fourth, having found that the reputation of complementary goods has the strongest impact on sales, we reiterate the recommendation in prior research [1] that a business with a social commerce strategy leverage the combined reputation of these products to promote new products. Since the reputation of complementary goods is social proof to consumers that a product is purchasable, we recommend that retailers emphasize this reputation to shoppers. However, this practice should be used carefully in a manner that does not dilute the reputation of complementary goods by linking them with products that customers eventually deem unsatisfactory. If this situation occurs, it could likely trigger ewom detrimental to the entire pool of complementary goods. There are a number of limitations to this research. First, reviews and ratings might not be accurate or truthful, even if they were done by trusted friends. Second, our study covers a time series of 56 weeks. One could argue that, if we were to prolong the longitudinal study, when enough reviews are posted, manipulation of ewom could actually hurt firms [13]. In other words, the effect of a more sustained social discussion through ewom should be further analyzed. Another limitation is the causality of our modeling. Although there appears to be a solid theoretical ground to argue that demand is a function of ewom for goods where price is low and fixed, we limited our study to

19 International journal of electronic commerce 109 prediction rather than to explanation. We suggest addressing causality issues in future research. Additionally, the items used to calculate the author rating and SP rating scores were limited to ten, and while practically sufficient to generate a proxy score, they may not be fully representative of the brand or complementary goods reputation. The use of the Amazon sales rank as a proxy for sales, although validated in previous research, adds an additional level of uncertainty in the measurement. In light of our findings regarding consumer decision making, we recommend that future social commerce research focus on the effect of a variety of social interaction types beyond ewom to include discussion forums in social networks, the ability to co-create reviews allowing friends to share their experience as a virtual group of consumers, observations of buying behaviors during synchronous shopping, and real-time reviews (e.g., shopwithyourfriends.com). For additional future research, we recommend exploring the distinction between ewom from strong ties such as friends and family in social network versus ewom from strangers, whether they are experts or casual, unknown shoppers (weak ties), since there are insufficient and some conflicting findings in this area, with some studies demonstrating the strength of weak ties [28]. In this study we picked Shorts as a rather homogeneous type of product (abbreviated books) with a rather like-minded group of readers. It is conceivable that the type of product for which reviews are sought is an influential factor in assessing the nature and role of social proof. For example, when shopping for an expensive or technically complex product, expert recommendation may be more important than that of a close but uninformed friend. The widespread adoption of social plugins such as Facebook Connect now allows for ewom providers to be identified by their strong-tie network, enabling the conduct of research in this area. Until newer social shopping technologies gain further adoption, ewom technologies should be considered by both e tailers and shoppers as the first and perhaps primary source of social proof. NOTES 1. Amazon.com discontinued the Amazon Shorts category of products after launching the Kindle and its related content. This concept has returned as Amazon Singles. 2. This rating is known as a similar products rating or complementary goods rating [1]. 3. When this longitudinal study began, 133 Shorts were available, and only these Shorts are used for this empirical study. 4. For both the brand reputation and the reputation of complementary goods, we did not test the impact of the volume of reviews as a signal of reputation since the means and variances differ considerably for each Short. A product in the brand or pooled portfolio may completely overwhelm the signals provided by other products in the same brand or pool. 5. A plot of the introduction of Shorts over time (not shown) fits a polynomial distribution almost perfectly (R 2 > 0.97). 6. This is the measure of the complementary goods reputation for Amazon Shorts. 7. However, products retrieved as part of the generating of the SP rating and the author rating are given an average customer rating of zero, since they are not

20 110 Amblee and Bui providing signals about the Short being evaluated and are indeed adding to the uncertainty about the Short. 8. The spike at about week 6 represents the Christmas shopping week, and the relative sales rank spikes due to an abrupt increase in sales of other products on Amazon.com. However, the difference between the means of the sales ranks for the two groups of Shorts remains. 9. Unlike the hypothesis test for the impact of valence, all Amazon Shorts were included here and not just those Amazon Shorts with customer reviews attached to them. 10. To measure fluctuation, we use six-sigma control charts, which calculate six standard deviations from the mean, three positive, and three negative. Data points outside of the six sigma region indicate a process that is out of control. Also, eight or more continuous data points on one side of the mean, even within the six sigma boundaries, indicate a process that is out of control. 11. The effect of the author rating and SP rating are remarkably similar, with nearly identical constants (1.37 million vs million) and similar parameter estimates ( million vs million). However, the model fit for the SP rating is much stronger and is better able to explain the sales rank for Shorts. REFERENCES 1. Amblee, N., and Bui, T. Can brand reputation improve the odds of being reviewed on-line? International Journal of Electronic Commerce, 12, 3 (summer 2008), Barnes, S.J., and Pressey, A. The virtual maven: A study of market maven behavior in physical, Web, and virtual world channels. In Proceedings of the Nineteenth AMA Summer Educators Conference: Enhancing Knowledge Development in Marketing. San Diego: American Marketing Association, 2009, pp Bounie, D.; Bourreau, M.; Gensollen, M.; and Waelbroech, P. The effect of online customer reviews on purchasing decisions: The case of video games. Working paper, Telecom ParisTech, Paris, Brynjolfsson, E., and Smith, M.D. Frictionless commerce? A comparison of Internet and conventional retailers. Management Science, 46, 4 (2000), Brynjolfsson, E.; Hu, Y.; and Smith, M.D. Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers. Management Science, 49, 11 (2003), Brynjolfsson, E.; Hu, Y.J.; and Smith, M.D. From niches to riches: The anatomy of the long tail. Sloan Management Review, 47, 4 (2006), Chang, K.T.T., Tan, B.C.Y., and Liang, X. Electronic word of mouth: An integration of social influence and identity. Paper presented at the Thirty- First International Conference on Information Systems, St. Louis, December 12 15, Chaudhuri, A., and Holbrook, M.B. The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. Journal of Marketing, 65, 2 (2001), Chen, P.Y.; Wu, S.; and Yoon, J. The impact of online recommendations and consumer feedback on sales. In Proceedings of the International Conference on Information Systems. Washington, DC: Association for Information Systems, 2004),