How to withdraw negative feedback on ebay

RESEARCH ARTICLE

STRATEGIC BEHAVIOR IN ONLINE REPUTATION SYSTEMS: EVIDENCE FROM REVOKING ON EBAY1

Shun Ye School of Business, George Mason University, 4400 University Drive, Fairfax, VA 22030 U.S.A. {sye2@gmu.edu}

Guodong (Gordon) Gao and Siva Viswanathan R. H. Smith School of Business, University of Maryland, College Park, MD 20742 U.S.A.

{ggao@rhsmith.umd.edu} {SViswana@rhsmith.umd.edu}

This study examines how sellers respond to changes in the design of reputation systems on eBay. Specifically, we focus on one particular strategic behavior on eBay’s reputation system: sellers’ explicit retaliation against negative feedback provided by buyers to coerce buyers into revoking their negative feedback. We examine how these strategic sellers respond to removal of their ability to retaliate against buyers. We utilize one key policy change of eBay’s reputation system, which provides a natural experimental setting that allows us to infer the causal impact of the reputation system on seller behavior. Our results show that coercing buyers to revoke their negative feedback through retaliation enables low-quality sellers to manipulate their reputations and masquerade as high-quality sellers. We find that these sellers reacted strongly to eBay’s announcement of a proposed ban on revoking. Interestingly, after the power of these strategic sellers is curtailed, we find evidence that they exert more efforts to improve their reputation scores. This study provides valuable insights about the relationship between reputation system and seller behavior, which have important implications for the design of online reputation mechanisms.

Keywords: Reputation mechanisms, online ratings, quality transparency, online auctions

Introduction1

Reputation systems play a critical role in electronic markets given the significant information asymmetry between sellers and buyers (Ba and Pavlou 2002; Dellarocas 2003). A wide variety of reputation systems have been designed and implemented to mitigate problems arising from information asymmetry, with eBay’s feedback mechanism being the most established and well-studied among them. In keeping with the importance of reputation systems for online markets, both practitioners and academic researchers have invested substan- tial efforts in examining the design of online reputation and

feedback mechanisms as evidenced by the growing number of studies in recent years (e.g., Aperjis and Johari 2010; Bolton et al. 2004; Cabral and Hortacsu 2010; Dellarocas et al. 2006; Melnik and Alm 2002; Resnick and Zeckhauser 2002; Resnick et al. 2006). The importance of such feedback and ratings for transaction partners has been well documented, and prior studies have shown that a seller’s reputation score has a significant impact on sales and price premiums (e.g., Houser and Wooders 2006; Resnick et al. 2006).

Clearly, the effectiveness of a reputation system critically depends on the behavior of the transacting partners (Dini and Spagnolo 2005). Given the importance of reputation, it is not surprising that opportunistic sellers try to “game” the system to boost their reputation scores. It has been inferred that a substantial percentage of buyers would rather remain silent

1Ravi Bapna was the accepting senior editor for this paper. Bin Gu served as the associate editor.

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than provide negative ratings to a seller due to fear of retalia- tion. Therefore, one critical mission for reputation system design is to promote desirable seller behavior. Up to this point, however, there have been few studies examining how sellers respond to changes in reputation system design.

Our study represents an effort to fill this gap in the literature. In particular, we focus on one strategic behavior within eBay’s reputation mechanism: sellers’ retaliation against buyers who left negative feedback to force buyers to revoke the feedback (hereafter, we call these sellers strategic sellers). Before May 19, 2008, eBay allowed “revoking”—the ability to withdraw negative feedback subsequent to mutual agree- ment between the buyer and the seller. While the ability to revoke negative feedback enables transacting partners to correct honest mistakes, it is also prone to abuse by strategic sellers. Specifically, after receiving a negative rating from a buyer, the seller could retaliate by giving a negative rating to the buyer, and then suggest that both transaction partners withdraw their negative ratings. Since negative ratings are very rare (typically less than 1 percent of total ratings) and carry significantly more weight than positive ratings (Resnick and Zeckhauser 2002; Standifird 2001), such revoking can be especially damaging to the effectiveness of the online reputa- tion system. Starting in May 2008, eBay banned the with- drawal of negative feedback, and disallowed sellers from leaving neutral and negative feedback for buyers, in essence eliminating the possibility of retaliation and revocation by opportunistic sellers. This key policy change provides a “natural experiment” setting that allows us to infer the causal effect of reputation system design on seller behavior with greater confidence.

Our study seeks to empirically examine how strategic sellers behave before as well as after eBay’s policy change, com- pared to nonstrategic sellers. Specifically, we examine two research questions: (1) How do strategic sellers differ from nonstrategic sellers in their reputation profiles before the policy change? (2) What are the impacts of the changes to eBay’s reputation system on the behavior of strategic sellers as compared to those of nonstrategic sellers? In answering the first question, we are able to assess the extent to which the availability of revoking benefits strategic sellers, but hurts the reputation system. We find that, prior to the policy change, strategic sellers had a superficially similar, but underneath significantly worse, reputation compared to a matched sample of nonstrategic sellers. Interestingly, we find that strategic sellers were also more likely to participate in the online strike initiated to protest against the change compared to nonstra- tegic sellers, perhaps indicative of their concern about the potential damage to their reputation from the proposed ban on

revoking. The second research question seeks to examine the differential impact of the policy change on strategic sellers and nonstrategic sellers. Using a difference-in-differences analysis, we find that while both types of sellers receive a higher percentage of negative ratings as expected, the magni- tude of the increase is much smaller for strategic sellers. Additional tests and robustness checks suggest that strategic sellers are more likely to improve their service quality after the policy change to compensate for their inability to use revoking as a strategic tool to fix their reputation. These findings not only provide one of the first pieces of empirical evidence of seller reactions to changes in the reputation mech- anism design, but generate valuable insights on the crucial behavioral assumption about how reputation systems should be modeled.

The remainder of the paper is structured as follows: In the next section, we provide an overview of existing literature. We then explain the data collection and the natural experi- mental setting. Next, we describe all of the major variables used in the econometric models, examine the differences between strategic sellers and nonstrategic sellers before the policy change, including their true reputation scores and their reactions to the announcement of the policy change, and focus on examining how they respond to the changes in the reputa- tion mechanism design (i.e., the elimination of revoking). Finally, we discuss the implications and present our conclusions.

Background

Online Reputation System

In online exchange markets like eBay, sellers and buyers are often geographically separated. The buyer has few means to verify the quality of the seller or hold the seller responsible. The potential of seller opportunism is even more significant when buyers and sellers have infrequent interactions. Reputa- tion systems, which disseminate information on the past behavior of individual traders, are designed to facilitate trustworthy transactions among strangers on the Internet. Numerous online markets, such as Elance.com, vWorker.com, Amazon.com, and eBay have adopted reputation mechanisms to promote honesty and better efforts in traders’ behavior.

Whereas an increasing number of studies have focused on designing different reputation mechanisms (e.g., Masclet and Pénard 2012; You and Sikora 2011), eBay’s reputation mechanism is arguably the most established and the most

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scrutinized by the popular press as well as by academics. On eBay, the primary source of information about the trust- worthiness of a seller is his/her feedback profile. Upon the completion of a transaction, both buyers and sellers have the opportunity to leave feedback within 90 days. Resnick and Zeckhauser (2002) find that buyers leave feedback 52.1 percent of the time and sellers leave feedback 60.6 percent of the time.

The feedback has three levels of valence: positive, neutral, and negative. In addition, buyers and sellers can each provide detailed comments about the other party regarding the transaction. The feedback a seller or a buyer receives is aggregated to calculate his/her feedback score, which is one key metric indicating the user’s reputation. A user’s reputa- tion score is calculated as the count of distinct users who gave positive feedback minus the count of those who left negative feedback, and it is displayed right next to the user’s ID wherever it appears on eBay. In addition, the percentage of positive feedback among all distinct positive and negative ratings for each seller is also reported. Since examining each individual feedback comment would entail a huge investment of time by the buyer, the reputation score, together with the percentage of positive feedback, is displayed to signal a seller’s quality. Given the importance of the feedback a user receives, eBay allowed buyers and sellers to negotiate to mutually revoke negative feedback ratings while unilateral attempts are disallowed. This policy has remained in place since eBay was founded in 1995, until the 2008 policy change that disallowed revoking.

Despite eBay’s popularity and success, there has been evi- dence of inefficiencies in its reputation mechanism. Some sellers continue to peddle fraudulent items with misleading descriptions without being caught. For instance, it is esti- mated that over 70 percent of the Tiffany jewelry sold on eBay is fake (Hafner 2007). Furthermore, one would expect an effective reputation mechanism to reward good sellers. However, researchers have failed to find consistent evidence for the impact of a seller’s reputation on auction price. Resnick et al. (2006), for example, find that negative feedback seems to have no impact on buyers’ willingness-to-pay. Cabral and Hortacsu (2010) examine sales of laptops, coins, and beanie babies on eBay and find that neither positive nor negative feedback influences the final auction price. Melnik and Alm (2002) find that even when a seller doubles his ratings, the consumer’s willingness-to-pay for gold coins in- creases by only 18 cents. Similarly, Lucking-Reiley et al. (2007) find that positive ratings have a negligible impact on price. This is echoed by Eaton (2005), who finds that a sel- ler’s reputation has little or no impact on the actual bid prices.

One critical issue that is detrimental to eBay’s reputation system is seller strategic behavior relating to feedback. On eBay, sellers and buyers may independently leave feedback within 90 days of the transaction and the feedback is available immediately to the other party. While the system is sym- metric (two-way), allowing both buyers and sellers to rate each other, buyers are at a disadvantage because they face product uncertainty before payment and seller opportunism after payment. While the reputation system intends to facili- tate buyers’ reporting of dishonest sellers to warn others, the symmetric nature of the previous reputation system makes it convenient and nearly costless for sellers to retaliate against any buyer providing them a negative rating. Thus it was apt to say that for buyers, “a negative first feedback can never be given without the fear of retaliation” (Klein et al. 2009, p. 315). This fear of retaliation reduces a buyer’s propensity to leave negative feedback on the seller (Dellarocas and Wood 2008). As a result, this creates an incentive for one party to strategically withhold its feedback as a means of retaliation (Dellarocas and Wood 2008; Yamagishi and Matsuda 2002). In addition to this direct feedback retaliation, a seller can also threaten to report buyers as scammers or abusers of the feedback system as a way to discourage negative feedback. This happens through private messaging and is not directly observable.

Once a buyer leaves a negative rating, the seller can retaliate and then try to “fix” the feedback using eBay’s revoking policy (Bolton et al 2009; Klein et al. 2009). In the vast majority of cases, revoking (the withdrawal of feedback based on mutual agreement) is preceded by a reciprocal negative feedback. When a seller responds to a negative rating with a negative rating, about 27 percent are later withdrawn through the revoking mechanism (Bolton et al. 2009).

In summary, the ability to retaliate and revoke feedback cre- ates an incentive for opportunistic sellers to manipulate their reputations by nullifying negative feedback. Whereas Bolton et al. (2009) and Klein et al. (2009) have pointed out the possibility of such strategic revoking, no study has thus far empirically and systematically examined this phenomenon.

eBay’s Policy Change: Ending Seller Coercion

Given the potential problems of eBay’s reputation system, scholars have suggested different ways to enhance the design of reputation systems. In a theoretical analysis, Ba et al. (2003) suggest that digital certificates issued by a trusted third party can motivate market participants to behave honestly. Others have also proposed that eBay should allow only the

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buyer to rate the seller (Chwelos and Dhar 2006) or that eBay should simultaneously reveal both partners’ ratings (Reichling 2004). Eventually, in January 2008, eBay announced dra- matic changes to its reputation mechanism, and starting on May 19, 2008, sellers were no longer allowed to provide negative or neutral feedback to buyers. A seller now has only two choices: not leaving any feedback, or leaving positive feedback to the buyer. Furthermore, revocation or mutual withdrawal of the feedback was disallowed. Any feedback that is left cannot be removed unless it is investigated and determined as a violation or abuse of eBay’s feedback policy after a dispute is filed. Bill Cobb, CEO of eBay, made the following comments in his public announcement on the reputation mechanism changes:

The original intent of eBay’s public feedback system was to provide an honest, accurate record of member experiences....But overall, the current feedback system isn’t where it should be. Today, the biggest issue with the system is that buyers are more afraid than ever to leave honest, accurate feedback because of the threat of retaliation. In fact, when buyers have a bad experience on eBay, the final straw for many of them is getting a negative feedback, espe- cially of a retaliatory nature.

Now, we realize that feedback has been a two-way street, but our data shows a disturbing trend, which is that sellers leave retaliatory feedback eight times more frequently than buyers do...and this figure is up dramatically from only a few years ago.

So we have to put a stop to this and put trust back into the system. (eBay 2008)

This change—from a symmetric to an asymmetric feedback system—removed a seller’s ability to retaliate against a buyer providing negative feedback. This change serves as an exoge- nous event that enables us to investigate how different sellers (both strategic and nonstrategic sellers) respond to the pro- posed as well as the actual changes in the design of eBay’s reputation system. The change in the reputation system shields buyers from retaliation by the sellers; hence they should be more willing to express their negative opinions about sellers. As for sellers, since the policy change mostly affects strategic sellers who have used retaliation and revoking to fix their reputations, they should be the most affected by the new policy. If so, these strategic sellers should be more likely to express their displeasure to the policy change. Further, if these sellers continue to under- perform, they could easily attract more negative feedback than

other sellers under the new reputation mechanism. Therefore, this policy change offers a valuable opportunity to examine how strategic sellers respond to reputation system design, which we examine in the remainder of this paper.

Research Context

eBay’s radical overhaul of its reputation mechanism, described above to be effective in May 2008, was announced on January 30, 2008. We examine the period before and after this policy change. To allow enough time for the new reputa- tion mechanism to take effect, we define a 3-month period— July 1, 2008, to September 30, 2008—as the post-change period.2 Correspondingly, we define July 1, 2007, to Sep- tember 30, 2007, as the pre-change period for two reasons. First, the pre- and post- periods cover the same months of a year, which alleviates potential seasonal effects on seller behavior. Second, because the pre-change period ends four months before eBay’s announcement, it is unlikely that buyers and sellers had changed their behavior in anticipation of the policy change. Comparing the pre- and post- periods allows us to examine the impact of the change in the reputation system design on seller behavior. Figure 1 shows the timeline of the events.

We draw a random sample of 2,890 sellers from the eBay marketplace (which we refer to as “general sellers”).3 To control for product categories, the sampling is based on the distribution of products listed on eBay. From this random sample of general sellers, we identify strategic sellers and nonstrategic sellers and examine how they respond to the policy change differently.

In addition to a comparison of strategic sellers and nonstra- tegic sellers during the pre- and post-change periods, we also

2eBay instituted additional changes in October 2008. For example, eBay stopped allowing users to send checks or money orders as payment for items purchased on the U.S. version of the site after October 20, 2008. Buyers would only be able to pay using PayPal, ProPay, credit or debit cards (if the seller had an Internet merchant account), or pay for the item upon pickup. These changes are beyond our study period, and thus they should not interfere with the effect of feedback policy change on seller behavior in our study.

3We restrict our sample to well-established sellers with total lifetime feed- back of 500 or more at the time of data collection in the year 2008. This reduces the noise from casual sellers and allows for a more accurate measurement of seller behavior based on transaction volume. These sellers account for 69.98 percent of all active listings on eBay at the time of our data collection.

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Figure 1. Timeline of eBay’s Reputation System Change

exploit an unusual event that provides further insights on seller reactions to the announcement of the policy change. The announcement of the policy change caused outrage among some sellers and culminated in a week-long strike, from February 18 to February 25, 2008, to protest the changes (Zouhali-Worrall 2008). In keeping with our primary objec- tive of understanding the differences in behaviors between strategic sellers and nonstrategic sellers before as well as after the policy change, we collect data relating to this strike to examine whether strategic sellers are more likely to partici- pate in the strike compared to other sellers.

We use eBay’s seller central forum to identify the sellers who participated in the strike. This forum is an online space for sellers to discuss a variety of issues related to eBay sellers, and it was established several years before the strike. Fol- lowing the announcement of the policy change in January 2008, a thread on eBay’s seller central forum was created with the title “Sign the pledge: No sales Feb 18-25!” From this thread we identify 398 unique IDs of sellers who signed

the pledge, whom we refer to as “strikers.” From this group of naturally disclosed sellers, we also identify strategic sellers and nonstrategic sellers.

For all of the sellers, we collect two sets of data: sellers’ feedback history and sellers’ listing records. The data covers all listings (including sold and unsold items) for the years 2007–2008, as well as the feedback if received. Based on sellers’ feedback history data, we calculate each seller’s pro- file, including their reputation scores and specific types of feedback ratings, which are dynamically updated at the time of each listing.

Our main analyses focus on the differences between strategic sellers and nonstrategic sellers, which are identified from the above sources.4 Since our focus is on the sellers’ revoking

4To increase the generalizability of our findings, we also conduct all of the analyses on general sellers and strikers separately and obtain consistent results.

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behavior, we differentiate between the cases of seller retali- ated and revoked (SRR), buyer retaliated and revoked (BRR), and non-retaliated and revoked (NRR). SRR feedback refers to the situation wherein the buyer leaves the seller a negative rating followed by the seller retaliating with a negative rating, and then both parties mutually agreeing to revoke their nega- tive feedback. BRR feedback refers to the situation wherein the seller leaves the buyer a negative rating followed by the buyer retaliating with a negative rating, and then both parties mutually withdrawing negative feedback. NRR feedback refers to the situation wherein the buyer gives the seller a negative rating and the seller directly asks for a withdrawal without any retaliation.

Because only SRR feedback reflects sellers’ strategic retali- ation behavior, we define strategic sellers as sellers who had SRR feedback in the pre-change period (before the announce- ment of the policy change). Nonstrategic sellers are sellers with zero SRR feedback (but they may have a small propor- tion of BRR or NRR feedback). This results in a sample of 387 strategic sellers (221 from general sellers and 166 from strikers) and 2,901 nonstrategic sellers.

Analyses

Data Description

We first examine how strategic sellers differ from nonstra- tegic sellers before the policy change and then examine how strategic sellers respond to the policy change differently from nonstrategic sellers. Table 1 provides a full list of all the dependent variables and explanatory variables that are used in different regression specifications.

Comparison of Strategic Sellers and Nonstra- tegic Sellers Before the Policy Change

Before the Announcement: The Effect of Revoking on Seller Reputation

Before examining how the policy change affects strategic sellers’ behavior, it is important to assess the extent of the benefit these sellers derive from revoking. If revoking plays a major role in affecting these sellers’ reputation, then it is more reasonable to assume that disallowing revoking should affect seller behavior in a substantial way. Therefore, we examine (1) the extent to which revoking contributes to boosting the displayed reputation scores of strategic sellers; and (2) how the displayed and real reputation scores of strategic sellers compare to the reputation of nonstrategic sellers.

Because SRR feedback is relatively rare, observing a higher percentage of SRR feedback for strategic sellers requires that they have a significantly higher number of feedback ratings than nonstrategic sellers. Therefore, it is not surprising that the average reputation score for strategic sellers (559.76) is higher than that of the nonstrategic sellers (149.71). This result is also consistent with the findings of Wood et al. (2002), which show that sellers with high reputation scores are more likely to engage in opportunistic behavior because buyers have a higher tolerance for them.

To confirm that our findings are not driven by the difference in the number of feedback ratings or other seller character- istics, we use the propensity score matching method to correct for potential sample selection bias due to the observable differences (Dehejia and Wahba 2002). We first predict propensity score based on a logit regression of the treatment (i.e., the status of being a strategic seller) on several key covariates, including the seller’s reputation score, the seller’s tenure on eBay, the average product price of the seller’s listings, and if the seller is a Powerseller5 or not. Then, for each strategic seller in the treatment group, we identify a matching seller in the control group (i.e., nonstrategic sellers) using nearest neighbor matching on the propensity score. Common support condition is imposed so that the treatment observations whose propensity scores are higher than the maximum or less than the minimum propensity score of the controls are dropped. This results in 354 strategic sellers and 354 nonstrategic sellers.

On eBay, a seller’s displayed reputation is reflected in his/her reputation score and in the percentage of positive feedback. Reputation score is defined as the number of unique positive feedback subtracted by the number of unique negative feed- back.6 The displayed percentage of positive feedback for a given seller is calculated by dividing the number of unique positive ratings by the total number of unique positive ratings and unique negative ratings. Once a feedback is revoked, it is not included in the calculation of reputation score and per- centage of positive feedback. Therefore, the displayed reputa- tion is subject to gaming. We further calculate a seller’s “true reputation” by taking into account neutral and revoked feedback.

5A Powerseller is an eBay seller who participates in the Powersellers program and maintains a high quality feedback profile and constant or growing trading volume. Powersellers enjoy a closer trading relationship with eBay, in- cluding increased attention, specialized tools, and discounts on final value fees.

6Consistent with eBay’s approach to calculate reputations, we only consider unique feedback: multiple positive feedback ratings from the same buyer are counted as only one positive feedback rating. Other types of feedback ratings are treated similarly.

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Table 1. Description of Variable Variable Name Variable Description

ifStrike 1 if the seller participated in the strike; 0 otherwise NumberofListingsLog The seller’s number of listings in one month prior to the strike (logrithamized) ifPowerSeller 1 if the seller is a PowerSeller on eBay; 0 otherwise SellerTenure How many months the seller had stayed on eBay FeeDifference The financial loss a seller would suffer under the new policy for one month’s listing ReputationScoreLog The seller’s reputation score (logrithamized) TotalNegativePct The seller’s percentage of initial negative feedback (with revoked feedback counted

as negative feedback) RemainingNegativePct The seller’s percentage of negative feedback RevokedFeedbackPct The seller’s percentage of revoked feedback SRRFeedbackPct The seller’s percentage of seller retaliated and revoked feedback BRRFeedbackPct The seller’s percentage of buyer retaliated and revoked feedback NRRFeedbackPct The seller’s percentage of non-retaliated and revoked feedback ifFeedbackNegativeit 1 if the feedback received by seller i at time t is negative; 0 otherwise AfterPolicyChanget 1 if the time t is after the policy change; 0 otherwise StrategicSelleri 1 if the seller i is a strategic seller; 0 otherwise MonthDummyt Dummy variables for months MonthIsAugt 1 if the time t is in August; 0 otherwise MonthIsSeptt 1 if the time t is in September; 0 otherwise TransDurationit How long the transaction for seller i at time t took to complete TransPriceLogit The final price of the transaction for seller i at time t (logrithamized) SellerTenureit The seller i’s number of months on eBay at time t MonthlyNegativePctim The seller i’s aggregated monthly percentage of negative feedback at month m ifFeedbackPositiveit 1 if the feedback received by seller i at time t is positive; 0 otherwise HonestRevokeri 1 if the seller i is a honest revoker; 0 otherwise

Table 2. Pre-Change Overall Reputation Profile Comparison: Strategic Sellers Versus Nonstrategic Sellers

Displayed Reputation† True Reputation

Score Positive Negative Positive Negative Neutral Revoked eBay-Withdrawn Strategic Sellers 564.99 99.58% 0.42% 97.85% 0.41% 0.64% 0.99% 0.11%

Nonstrategic Sellers

521.91 99.65% 0.35% 98.93% 0.34% 0.51% 0.13% 0.09%

T-value 0.57 -1.09 1.09 -7.98*** 1.08 2.10* 17.41*** 1.04 †eBay displays the percentage of positive feedback as the key metric of a seller’s reputation. Percentage of negative feedback is simply 1 minus the percentage of positive feedback. To be consistent with eBay’s practice, we only report the percentage of positive feedback and the percentage of negative feedback for “Displayed Reputation,” which is what a buyer observes. *p < 0.05; **p < 0.01; ***p < 0.001.

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Table 2 provides the comparison of both displayed reputation and true reputation profiles for strategic sellers and nonstrategic sellers. For the displayed reputation, the average percentage of positive feedback for strategic sellers and nonstrategic sellers is 99.58% and 99.65%, respectively. The difference is not significant at the 5% level, suggesting that the displayed reputation is similar between strategic sellers and nonstrategic sellers.

We next compare the true reputations of strategic sellers and nonstrategic sellers. Note that the revoked feedback was originally a negative feedback that had been withdrawn upon the mutual agreement of both the seller and the buyer. After adding in the original negative value of revoked feedback, we find that strategic sellers actually have a much higher true negative feedback percentage than nonstrategic sellers (0.99% + 0.41% = 1.40% for strategic sellers, and 0.13% + 0.34% = 0.47% for nonstrategic sellers, t-value = 10.51, p < 0.001). Combined with the comparison of the displayed reputations, our results indicate that while strategic sellers have a much higher percentage of true negative feedback, the revoking mechanism helps these lower-reputation sellers masquerade as sellers with higher reputations.

Are Strategic Sellers More Likely to Participate in the Strike Against the New Policy?

Given the evidence above that revoking can be used as a tool to strategically nullify negative feedback, in this section we seek to examine if there was a significant difference in the propensity of strategic sellers to participate in the strike, compared to nonstrategic sellers.

Since the strike was initiated in the eBay forum, one may argue that sellers active in the forum were more likely to strike merely because they knew about it. To control for this potential confounding factor and ensure the robustness of our results, we introduce a control group in our analysis on the strike propensity: forum sellers who were active in the forum but did not participate in the strike. We create a random sample of 2,280 such sellers (which we refer to as forum sellers), and analyze them together with general sellers and strikers in predicting strike propensity.

To confirm that the sellers who pledged to join the strike actually participated in the strike, we check their listing activities during the strike week. We do find that strikers reduced their listings significantly during the one-week period whereas we observe no such trend for general sellers and forum sellers.

In addition to the changes in the reputation system, there are other factors that might drive participation in the strike. Spe-

cifically, at the same time eBay announced changes to its fee structure, with lower listings fees (the price charged for each item listed to be sold on eBay) and higher final value fees (a percentage of the closing price extracted by eBay). Based on their listing and sales patterns, some sellers believed that they would have to pay more because of these changes. Thus, potential financial loss under the new fee structure could have also motivated some sellers to join the strike.

To control for the potential impact of changes in the fee structure, we collect detailed listings of sellers in all three groups one month prior to the strike (from January 18, 2008, to February 17, 2008). We collect detailed information about each listing, including product category, auction style, starting price, final price, and usage of features such as gallery pic- tures and subtitles. This allows us to calculate the exact fee charged by eBay. To measure potential financial loss, we calculate, for each listing, the difference between fees actually charged by eBay under the old fee structure and fees that would be charged by eBay under the new fee structure. We then aggregate the differences at the seller level.

In addition to changes to the fee structure, several other factors could potentially influence participation in the strike as well. Sellers with a larger number of listings (logarith- mically) would suffer more financially if they joined the strike and hence may have been less likely to participate. Powersellers would also be less likely to join the strike because they would enjoy significant final value fee discounts under the new fee structure. The longer a seller has used eBay, the higher his/her switching cost due to the accumu- lated loyal customer base on eBay. These sellers should have a stronger reaction to the reduction of seller power under the new reputation mechanism. Therefore, we included number of months on eBay as another control variable. Seller repu- tation is measured by both reputation score (log-transformed) and total negative feedback percentage (i.e., the sum of revoked negative feedback percentage and remaining negative feedback percentage). The full specification of the model is

Logit (ifStrike) = α + β1 × NumberofListingLog + β2 × ifPowerSeller + β3 ×SellerTenure + β4 × FeeDifference + β5 × ReputationScoreLog + β6 × TotalNegativePct + g

The descriptive statistics and correlation matrix of the variables in the regression are provided in Tables 3 and 4. The maximum VIF is 1.59, well below the threshold of 10, indicating that there is no multicollinearity among the independent variables.

The results of the logit regression model are shown in Table 5. Model 1 is the baseline model. The coefficient of fee

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Table 3. Summary Statistics for the Strike Analysis

Variable Number of

Observations Mean Std. Dev. Min Max (1) NumberofListingLog 5568 4.00 2.15 0.00 9.19

(2) ifPowerSeller 5568 0.43 0.50 0.00 1.00

(3) SellerTenure 5567 76.77 30.58 5.73 145.83

(4) FeeDifference 5568 -21.38 89.66 -699.02 1715.68

(5) ReputationScoreLog 5568 4.46 1.19 0.00 9.61

(6) TotalNegativePct 5568 0.27% 0.10% 0.00% 28.57%

(7) RemainingNegativePct 5568 0.26% 0.96% 0.00% 25.00%

(8) RevokedFeedbackPct 5568 0.14% 0.61% 0.00% 25.00%

(9) SRRFeedbackPct 5568 0.08% 0.39% 0.00% 10.00%

(10) BRRFeedbackPct 5568 0.02% 0.20% 0.00% 6.67%

(11) NRRFeedbackPct 5568 0.04% 0.42% 0.00% 25.00% (12) ifStrike 5568 0.07 0.26 0.00 1.00

Note: There is one missing value for number of months on eBay.

Table 4. Correlation Matrix Variable VIF (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (1) 1.39 1.00 (2) 1.29 0.36* 1.00 (3) 1.04 0.02* -0.07* 1.00 (4) 1.16 -0.28* -0.16* 0.07* 1.00 (5) 1.59 0.45* 0.45* -0.18* -0.19* 1.00 (6) 1.01 0.01* 0.04* -0.05* -0.01 0.00 1.00 (7) 1.01 0.03* 0.01 -0.03* -0.00 -0.02 0.86* 1.00 (8) 1.01 0.06* 0.06* -0.04* -0.02 0.04* 0.58* 0.07* 1.00 (9) 1.01 -0.02 0.05* -0.03* -0.03* 0.08* 0.38* 0.07* 0.63* 1.00 (10) 1.00 -0.00 -0.01 -0.02 0.01 -0.01 0.19* 0.02 0.33* -0.00 1.00 (11) 1.00 0.01 0.03* -0.02 -0.01 -0.02 0.38* 0.03* 0.69* 0.00 -0.00 1.00 (12) 0.03* -0.02 0.05* 0.04* -0.04* 0.07* -0.01 0.16* 0.27* 0.01 -0.01 1.00

Note: Pair-wise Spearman correlation is reported. *Indicates p < 0.05.

difference is significantly positive, suggesting that sellers who stand to lose more (or save less) under the new fee structure are more likely to strike. Consistent with our prediction, sellers with a longer tenure on eBay are more likely to strike. Powerseller status and the volume of listings do not have a significant effect on a seller’s propensity to strike. The coefficient of total negative feedback percentage is significantly positive, suggesting that sellers with more negative feedback before revoking are more likely to strike. In Model 2, we divide total negative feedback percentage into remaining negative feedback percentage and revoked feed-

back percentage in the regression. We find that the pseudo R2 increases by almost 160 percent, supporting the assertion that a seller’s revoking behavior has significant explanatory power on his/her participation in the strike. The coefficient of the percentage of revoked feedback is positive and is significant at the p < 0.001 level. This suggests that sellers with a history of revoking negative feedback are more likely to strike.

In Model 3, we split revoked feedback into SRR feedback, BRR feedback, and NRR feedback. The Pseudo R2 further increases by about 100 percent. The coefficient of SRR feedback percentage is significant and positive, but the coeffi-

MIS Quarterly Vol. 38 No. 4/December 2014 1041

Ye et al./Strategic Behavior in Online Reputation Systems

Table 5. Logit Regression Analyses of Strike Propensity Dependent Variable: ifStrike

Model 1 Model 2 Model 3

Independent Variable coefficient (std. err.)

coefficient (std. err.)

coefficient (std. err.)

Intercept -2.768*** (0.273)

-2.767*** (0.278)

-2.607*** (0.286)

NumberofListingsLog 0.005 (0.029)

0.009 (0.030)

0.000 (0.031)

ifPowerSeller -0.006 (0.121)

-0.011 (0.122)

0.017 (0.126)

SellerTenure 0.007*** (0.002)

0.007*** (0.002)

0.007*** (0.002)

FeeDifference 0.002* (0.001)

0.002** (0.001)

0.002** (0.001)

ReputationScoreLog -0.088 (0.053)

-0.108* (0.055)

-0.158* (0.057)

TotalNegativePct 13.958** (3.029)

RemainingNegativePct -15.667* (7.791)

-24.764* (9.469)

RevokedFeedbackPct 69.149*** (7.098)

SRRFeedbackPct 166.126*** (11.073)

BRRFeedbackPct 15.029 (19.976)

NRRFeedbackPct -52.169 (33.837)

Pseudo R2 0.017 0.045 0.098

*p < 0.05, **p < 0.01, ***p < 0.001

cients of BRR feedback percentage and NRR feedback percentage are insignificant. This indicates that sellers who strategically retaliate and then revoke negative feedback are indeed more likely to strike. A 0.1 percent increase in SRR feedback percentage would lead to 18.07 percent increase in the odds of joining the strike.

The logit regression analyses on the strike provide empirical evidence that revoking after retaliation is a significant factor that motivates the participation in the one-week strike: since strategic sellers will lose a strategic tool to deceptively “boost” their reputation after the policy change, they are more likely to protest against the ban on revoking.

Comparison of Strategic Sellers and Non- strategic Sellers after the Policy Change

Given the initial evidence that strategic sellers are more likely to demonstrate their displeasure by joining the strike, in this

section we focus on the impact of reputation system change on strategic sellers compared to nonstrategic sellers by utilizing the random sample of eBay sellers as well as the sample of strikers.7 Our major analyses, as detailed below, focus on how strategic sellers differ from nonstrategic sellers in the efforts they exert to reduce the chance of receiving negative feedback8 when moving from the pre-change period to the post-change period.

7We also conduct analysis using the strikers as the convenient sample of strategic sellers and the other sellers as the control group of nonstrategic sellers and get similar findings.

8Revoked feedback in the pre-change period is converted to their original values and count as negative feedback.