An Empirical Analysis of Consumer Behavior in the Online Auction Marketplace

Transcription

1 Clemson University TigerPrints All Theses Theses An Empirical Analysis of Consumer Behavior in the Online Auction Marketplace Calvin Salter Clemson University, Follow this and additional works at: Part of the Economics Commons Recommended Citation Salter, Calvin, "An Empirical Analysis of Consumer Behavior in the Online Auction Marketplace" (2014). All Theses This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact

2 AN EMPIRICAL ANALYSIS OF CONSUMER BEHAVIOR IN THE ONLINE AUCTION MARKETPLACE A Thesis Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Master of Arts Economics by Calvin Salter December 2014 Accepted by: Robert D. Tollison, Committee Chair Scott L. Baier Raymond D. Sauer

3 ABSTRACT The purpose of this paper is to analyze a unique data set pertaining to ebay auctions, in particular auctions of the first generation ipad. This analysis will give some interpretations about consumer behavior in the consumer electronics market on ebay, especially how the reputation of sellers and the shipping price that sellers charge affects the final selling price of an item. Other economics variables are analyzed to provide a broader picture of pricing determinants. Reputation is an important mechanism to consider in any market. However, reputation in the ebay market is particularly important because buyers do not interact with the sellers and do not physically handle the products. The paper finds that reputation is an important factor in the final selling price of auctions on ebay. ii

4 TABLE OF CONTENTS Page TITLE PAGE... i ABSTRACT... ii LIST OF TABLES... iv LIST OF FIGURES... v CHAPTER I. INTRODUCTION... 1 II. DESCRIPTION OF THE DATA SET... 7 III. FORMULATION OF THE ECONOMETRIC MODELS IV. ESTIMATIONS OF THE ECONOMETRIC MODELS V. CONCLUSION APPENDIX REFERENCES iii

5 LIST OF TABLES Table Page 1 Full Summary Statistics Conditional Summary Statistics Heteroskedasticity Results Model 1 Results Model 2 Results Model 3 Results Model 4 Results Model 5 Results iv

6 LIST OF FIGURES Figure Page 1 First Derivative Second Derivative Total Revenue v

7 CHAPTER ONE INTRODUCTION ebay contains a wealth of information about buyers and sellers. It also serves as a communication hub for which buyers and sellers are paired together based on individual preferences. These preferences consist of price point, shipping price, item condition, and various other variables including the ebay feedback system. Buyers and sellers both have one but the seller s feedback profile is the most important. This is due to the fact that buyers see the seller s profile and choose whether or not to bid on their product. In other words, the buyer chooses the seller and the seller does not choose the buyer. This is just the nature of online auctions. The ebay feedback system represents the reputation of ebay users. Thus, when testing how important reputation is, the information will come from the seller s ebay feedback profile. In this paper the seller s reputation, the shipping price, and other variables are examined to show what determines the pricing of auctions. This analysis will provide information into consumer behavior on the online auction marketplace. The auctions that are examined in this paper are of first generation Wi-Fi only ipads. In the paper econometric analysis is used to find pricing determinants and how they function. The seller feedback profile is important to go over due to the details that are involved. It contains the number of positive, neutral, and negative feedback received by the user. Buyers and sellers can both leave feedback to one another or choose not to. Sellers cannot leave negative feedback because before this policy was put into place buyers feared leaving negative feedback to sellers due to the retaliation effect. The 1

8 retaliation effect is when the seller would leave a negative feedback just because the buyer left a negative feedback. This policy promotes honesty among the buyers on ebay. There is also a feedback percentage which is. The particular feedbacks that go into this formula are from the past year of feedback. There are also detailed seller feedback ratings. These consist of four different categories. The first category is Item as Described which shows how well the seller describes their items. It helps the buyers know if the seller is perhaps dishonest in how they list their items. The second category is Communication which shows how well the seller communicates to the buyer. The third category is Shipping Time which shows how timely the shipping is completed to the buyer. Finally, the fourth category is Shipping and Handling Charges which shows if the seller is a cheap or expensive shipper to the buyer. The ratings are on a one to five star scale. The detailed feedback ratings only show up to the buyer whenever the seller has ten or more that the seller has earned (ebay). There is past economic literature relating to ebay, in particular the feedback system or reputation system, which is useful to go over as well. Advertisement quality is also important on ebay. Buyers make their decisions based on the quality of the posting or advertisement that a seller posts on the auction listing. According to Klein (1981) a producer will only sell as advertised assuming the present discounted value of all future sales is greater than the one-time increase in profit caused by cheating. This raises the interesting point that sellers on ebay are only as trustworthy as what they foresee in the future. However, in the ebay marketplace we are 2

9 not only dealing with tried and proven sellers but also one time sellers. In other words, buyers do not go to sellers based on their past history with those sellers but based on price and other economic variables. This finding raises the importance of an efficient feedback system because that is all buyers have to rely on when making their purchasing decisions. Klein and Leffler assume a firm in their paper but on ebay there are sellers with a range of size. There are sellers that are just starting to those that have had thousands of transactions. Also, these sellers sell to different buyers, so the only mechanism that holds them accountable is the ebay feedback system. Seller effort is another important component to the ebay feedback system. Sellers should be rewarded for their increase in positive ebay feedback. Cabral (2004) discovers the issue of sellers simply buying feedback. This does not mean that the sellers literally bought the feedback but that they would purchase items and receive positive feedback from those transactions. Since the overall feedback score is based on transactions as a seller or a buyer; this can be an issue. It undermines the goal of protecting buyers from poor sellers due to sellers artificially raising their feedback ratings. The paper also finds that whenever a seller receives their first negative feedback the effort level of that seller decreases. Thus, causing a vicious cycle of negative feedback left for that seller. Negative feedback essentially can also measure effort level of a seller. Resnick (2001) did an empirical analysis on the effect feedback has on the probability that an item will sell and the final selling price of that item; it also examines if past behavior or feedback is a good predictor of future behavior. The paper used two logistic regressions on the probability of selling Beanie Babies and Rio MP3 players and 3

10 found that there was a positive effect to the probability caused by an increase in positive feedback. However, the paper found that the effect of feedback on the final bid amount was insignificant. Finally, the paper discovered that the more experienced a seller is, the higher the probability of a good transaction. In essence, experience was determined to be a better indicator of a good transaction over positive feedback. However, past positive feedback did raise the probability of a good transaction. There are possible problems with the feedback system as highlighted by Dellarocas (2001). For instance, what if a buyer in a transaction, who is also an ebay seller, has a bad experience? If the system is well-functioning, then the buyer should leave negative feedback for that particular seller. However, that buyer may leave no or positive feedback so that the seller will not retaliate by leaving them negative feedback. The paper also reports that ebay has created a culture of praise, suggesting that some of the positive feedback may have been given just to maintain one s own reputation. This problem has been mostly fixed due to the ebay policy that doesn t allow sellers to leave negative feedback. Another interesting thought is that buyers also receive feedback, implying that someone who is a good buyer may be a bad seller even though their feedback reflects highly of them. Reeves (2006) conducted research on pennies or the US cents category on ebay. They specifically looked at the determinants of the final selling prices of different denominations of US pennies. They found that seller feedback did have a considerable effect on ebay auction prices. However, negative feedback had a more negative impact on price than positive feedback had on price. Reserve prices were also considered in their 4

11 research. On ebay one can set a reserve or a minimum bid that must be reached before the auction is allowed to end. In the paper they discovered that a reserve price tended to have a positive impact on the final selling price of an auction. However, the reserve price also resulted in a number of losses in sales. This is primarily due to the fact that a reserve price or minimum bid may have been set too high. Finally, the paper discusses the length of auctions and its effect on prices. The longer the auction led to a higher price. I think that this is fairly reasonable due to the fact that it gives more time for more bidders to enter into the auction. Eaton (2002) examined high end guitar auctions and the effects of incomplete information on the final bid price. They find that the use of pictures greatly increases the average value of high end guitars. This is mainly due to the fact that guitars are valued by their looks and the sound quality of the product. The one interesting finding that the paper discusses is that the presence of negative feedback raised the value of high end guitars, ceteris paribus. They offer the explanation that a more experienced seller is more likely to have a negative feedback, therefore the buyer has less to worry about due to the experience of the seller. The second explanation is that negative feedback may encourage communication between the buyer and seller, thus making a more intimate buying experience for the buyer. Wooders (2000) built an empirical model about how reputation affects the selling prices of high end computers. They find that the seller s reputation has a statistical and economic effect on the selling price. In other words there is a positive correlation between better reputation or feedback and the final selling price. The paper also reports 5

12 that the buyer s reputation does not have an economic impact on the selling price. This makes sense because the buyers are the ones choosing to bid. Sellers do not choose their buyers so the buyer s reputation should not have an economic impact on the final selling price. Morgan (2006) examines potential problems in the ebay feedback system. The first problem is that when buyers assess the feedback of a seller there is no way to distinguish the feedback of legitimate transactions and ones that are less important such as a purchased transaction. The second problem is that detailed item descriptions and feedback are only available for the past ninety days. Therefore, sellers can take advantage of the short time horizon. Finally, there is incentive to the buyer to not leave negative feedback to the seller due to retaliatory tactics that are employed by the seller in return. Namely, the seller simply gives a negative feedback in return due to the negative feedback that they received. Thus, buyers are less likely to be honest in their feedback giving. Once again, the new ebay policy does not allow sellers to leave negative feedback which should alleviate this problem. The next chapters are as follows: the description of the data set, the formulation of the econometric models, the estimations of the econometric models, and the conclusion. 6

13 CHAPTER TWO DESCRIPTION OF THE DATA SET There are no publicly available data sets of ebay data, which presented a problem. One could manually collect the information; however there are better ways to retrieve the information. Therefore, the data set for this paper was collected from ebay using a spider program created in R. This involved collecting the urls associated with the auctions of first generation ipads then parsing those web pages. In other words, this would convert the urls into html code which allowed for collecting the data systematically. The data points in the data set each represent characteristics of an ebay auction. Those characteristics are the final selling price, feedback percentage, number of bidders, shipping cost, auction length, day of the week that the auction ended, and if the seller had detailed feedback. This auction data will give the empirical means to test the determinants of selling price on ebay. One variable that could have been included in the empirical analysis is the number of bids. The number of bids is left out because the number of bidders should capture the same economic significance (popularity of the auction). Below is a more detailed description of each of the variables: 1. final_selling_price is the dependent variable that represents the final bidding amount or the amount that the buyer must pay the seller, not including the shipping. 2. feedback_percentage is the percentage of positive feedbacks in the past twelve months, which is also called the feedback rating. This is our measure of reputation. The higher the feedback rating the better the reputation. 7

14 3. dummy_feedback_perfect is a dummy variable that is equal to one if a user has perfect or a 100% feedback rating and is equal to zero otherwise. This variable will give an all or nothing approach to measuring the reputation of the sellers. 4. dummy_detailed is a dummy variable that is equal to one if a user has detailed feedback and is equal to zero otherwise. Detailed feedback is a relatively new system on ebay. It is essentially designed to give buyers a more detailed look into the seller s past transactions in which the seller received feedback. Detailed feedback only shows up on the seller feedback profile when a seller has received ten or more detailed feedbacks. This is a good measure of the experience that a seller has. 5. int_detailed_perfect is an interaction variable between dummy_feedback_perfect and dummy_detailed that is equal to _ _ _. 6. nbidders is the number of bidders in a particular auction. This will measure the popularity and actions that buyers took to purchase the item. 7. final_ship_cost is the shipping amount that is charged by the seller to the buyer. 8. auction_length is the length of time that an auction is on ebay. These amounts range from one, three, five, seven, or ten days. One key fact about this is that ebay charges no extra fees for seven days or less but it does charge $0.40 for ten days. 9. dummy_sunday is a dummy variable that equals one when the day of the week that the auction ends is Sunday and zero otherwise. 8

15 10. dummy_monday is a dummy variable that equals one when the day of the week that the auction ends is Monday and zero otherwise. 11. dummy_tuesday is a dummy variable that equals one when the day of the week that the auction ends is Tuesday and zero otherwise. 12. dummy_wednesday is a dummy variable that equals one when the day of the week that the auction ends is Wednesday and zero otherwise. 13. dummy_thursday is a dummy variable that equals one when the day of the week that the auction ends is Thursday and zero otherwise. 14. dummy_friday is a dummy variable that equals one when the day of the week that the auction ends is Friday and zero otherwise. 15. dummy_saturday is a dummy variable that equals one when the day of the week that the auction ends is Saturday and zero otherwise. The observations are from November 30, 2013 to February 18, As stated before the auctions are of used 16 gigabyte, Wi-Fi only, first generation ipads. I chose to stick with one product to control for variations in product differences. One observation represents one auction with the associated economic variables. 9

16 Table 1: Full Summary Statistics Variable Obs. Mean Std. Dev. Min Max final_selling_price feedback_percentage nbidders dummy_detailed final_ship_cost auction_length dummy_sunday dummy_monday dummy_tuesday dummy_wednesday dummy_thursday dummy_friday dummy_saturday dummy_feedback_perfect int_detailed_perfect The feedback percentage has an average value of % which implies that many of the sellers have a good reputation. In fact, 65.6% have a perfect or 100% feedback rating. There are only 44.6% of the sellers that have detailed feedback, thus implying that 55.4% are inexperienced sellers from this data set. The interaction variable s statistics implies that 15.5% of the sellers are both experienced and have a perfect feedback rating. One interesting observation about the auction length is that none of the sellers used the 10 day auction length option. This is probably due to the fact that the option is an extra expense of $0.40. The most popular day of the week to end the auction is on Sunday. The final selling price has a range of $660, which clearly indicates product differentiation. This is caused by some of the auctions indicating a variety of levels of condition. For example, some of the ipads may be broken and sold for parts which would lead to much lower price levels. That explains the lower price levels but 10

17 why are there such high prices? This is caused by the fact that those auctions included other items besides the ipad. For example, the auction may include a keyboard or a television or any number of products. ebay only allows for auctions to be categorized under one product, which is why we have this problem. There are not categories for multiple item listings. To correct for this problem this paper will only use the final selling prices within one standard deviation of the mean selling price. Table 2: Conditional Summary Statistics Variable Obs. Mean Std. Dev. Min Max final_selling_price feedback_percentage nbidders dummy_detailed final_ship_cost auction_length dummy_sunday dummy_monday dummy_tuesday dummy_wednesday dummy_thursday dummy_friday dummy_saturday dummy_feedback_perfect int_detailed_perfect The conditional did not make any major changes to the other economic variables. Feedback percentage is close to the same average as the average of the full amount of observations, implying that once again the reputation scores are leaning upwards. Sunday is still the most popular day of the week to end the auction. This change in the 11

18 number of observations should keep the used ipad product as homogenous as possible. In the next chapter we will discuss the econometric models that are used in this paper. 12

19 CHAPTER THREE FORMULATION OF THE ECONOMETRIC MODELS This paper uses five econometric models. Each of the models uses ordinary least squares to estimate the coefficients. The dependent variable in the models is the final selling price. This economic variable was chosen to show the determinants of price in ebay auctions. The theoretical equations that represent the econometric models are below: Model 1: _ _ _ _ _ _ _ _ _ _ _ _ _ (1) Model 2: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ (2) 13

20 Model 3: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ (3) Model 4: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ (4) Model 5: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ (5) 14

21 For the first model, there are twelve explanatory variables: feedback_percentage, nbidders, dummy_detailed, final_ship_cost, auction_length, and six of the seven dummy variables that represent each day of the week. The dummy variable for Saturday is not included in the regression model because I expect that Saturday should have the highest average selling price. Thus, it would be interesting to compare the expected values of the other days to the highest average selling day of the week. The feedback rating is an explanatory variable in the model, and it was chosen to show the effects of a higher feedback rating on the final selling amount. This will show how reputation affects the final selling price. The shipping cost should have a negative impact on the final selling price because the buyer pays the shipping cost. If consumers are completely rational it should be a dollar for dollar negative correlation. The number of bidders should have the obvious effect of causing the final selling price to increase. Length of the auction should also have a positive effect but the interest lies in by how much of a positive effect it has on the selling price. The dummy variable for the detailed feedback will give us a good estimation of how much experience selling affects the final selling price. The second model contains only two differences, a squared feedback percentage term and a squared shipping cost term. This will tell us how the effects caused by the feedback rating and the shipping cost vary as those variables change, which provides a deeper understanding of the expected and marginal effects. The third model adds the shipping cost cubed as an explanatory variable. Once again, this will help with understanding how the effect of shipping cost on selling price changes as the shipping cost changes but will provide an added layer to the marginal effects caused by the 15

22 shipping cost. The fourth model replaces the feedback rating as a continuous explanatory variable and replaces it with the dummy feedback rating that measures if the feedback is perfect or not. This will put an emphasis on an all or nothing reputation, since the average feedback was leaning so close to perfect. The fifth and final model adds in an interaction variable between the detailed feedback and perfect feedback rating explanatory variables. This will provide a better comparison between those who have experience and those with perfect feedback. 16

23 CHAPTER FOUR ESTIMATIONS OF THE ECONOMETRIC MODELS In this chapter, the estimations of the models will be shown first, followed by a discussion of the particular model. Before we get into the estimations, we will first test each model for heteroskedasticity by using the Breusch-Pagan or Cook-Weisberg test for heteroskedasticity. The results from each of the tests are below: Table 3: Heteroskedasticity Results Probability > Chi- Model Number Square The null hypothesis of this test is that the model is homoscedastic and the alternative hypothesis is that the model is heteroskedastic. Since the p-values associated with each of the models are all very close to zero the null hypothesis of homoskedasticity should be rejected for each model. There are commands in Stata that take care of this issue of heteroskedasticity. As stated before, the number of observations in each of the estimations is 2,

24 Table 4: Model 1 Results Variable Coefficient Robust Std. Err. P-Value feedback_percentage nbidders dummy_detailed final_ship_cost auction_length dummy_sunday dummy_monday dummy_tuesday dummy_wednesday dummy_thursday dummy_friday constant This model has a value of , which implies that 4.28% of the variation in the final selling price is explained by the model. The explanatory variables are jointly statistically significant with an F-value of The reputation coefficient implies that the difference in the expected value of the final selling price between someone with a 0% feedback rating and a 100% feedback rating is about $5.56, all else equal. Thus, the feedback rating and the final selling price are positively correlated. All of the variables are statistically significant except for auction_length, dummy_monday, dummy_wednesday, dummy_thursday, and dummy_friday. This of course does not mean that they are worthless in interpretation. The estimated coefficient of the length of the auction is quite low and not statistically significant. All of the auction lengths that a seller can choose from are free except for the 10 day option that costs $0.40 extra. If we assume that this coefficient is 100% accurate then it means that, on average, if a 10 day auction is chosen, the seller can expect to make $ $ more than the 18

25 free 7 day auction, ceteris paribus. Thus, the seller could expect to lose $0.40 $ $ by making the decision to go with a 10 day auction. Even if the estimated coefficient was the population coefficient, it still wouldn t make sense for the seller to go with the longer auctions. However, there is always a possibility that with a different sample, the results could say the opposite. The day of the week dummy variables are interpreted in comparison to Saturday. If the day of the week that the auction ends is Sunday, then the regression tells us, all else equal, that the final selling price is $2.55 less than if it would have ended on Saturday. Apparently, Monday is a better day to end the auction than Sunday. The worst day to end the auction, according to the estimates of this model, is Tuesday. The coefficient of dummy_wednesday is low and not statistically significant, which probably means that Wednesday and Saturday have roughly equal expectations of the final selling price, all else equal. Also, since the other variables are negative, we can conclude confidently that these are the two best days to end an auction for first generation ipads. The only other interesting note about the auction end date is that I expected Saturday to be the best day, not Wednesday. Another perplexing finding is that the sellers with detailed feedback have a negative effect on the expected final selling price of about $4, ceteris paribus. The number of unique bidders has an obvious positive effect on the final selling price. Finally, the shipping cost has a negative correlation with the final selling price which is logical. Specifically, the results show that with a $1 increase in the shipping cost the final selling price declines by about $0.28, ceteris paribus. Thus it implies that it is advantageous to the seller to set the shipping cost of the auction to a higher amount because for each 19

26 dollar the seller gains about $0.72 in total revenue. The next model will provide more insight into this phenomenon. Table 5: Model 2 Results Variable Coefficient Robust Std. Err. P-Value feedback_percentage feedback_percentage_squared nbidders dummy_detailed final_ship_cost final_ship_cost_squared auction_length dummy_sunday dummy_monday dummy_tuesday dummy_wednesday dummy_thursday dummy_friday constant The value for model 2 is equal to The different effects and expectations described above are practically the same here, except for the interpretations behind reputation and the shipping cost. Let us first take a look at the feedback percentage. From theoretical equation (2) we know that this is true: _ _ _ 2 _ which implies that the estimated effect is _ _ _ _, thus, as the feedback rating increases, the positive effect of an increase in the feedback rating decreases. In other words, the positive effect on the final selling price caused by 20

27 reputation diminishes as reputation becomes better. This estimation also says that the difference in a 0% and a 100% feedback percentage on the expected final selling price is $ , which clearly doesn t make much intuitive sense and I doubt that this is correct. However, the diminishing effect of the feedback rating is probably a valid observation. The shipping cost has an interesting interpretation in this model as well. From (2), we can say that _ _ _ _ 2 _ _ implying that the estimated effect is _ _ _ _ _ _. Therefore, the effect of the shipping cost on the final selling price becomes less negative and eventually positive ( $ is the amount that makes the effect $0). If a seller were to take this regression as literal truth, they would charge the most shipping that they could because the model implies that the final selling price increases as the shipping cost increases with shipping costs greater than $ Intuitively, we know that this seems absurd. Therefore, in the next model we will hopefully shed some more light on this issue with a cubed shipping cost term included in the model. 21

28 Table 6: Model 3 Results Variable Coefficient Robust Std. Err. P-Value feedback_percentage feedback_percentage_squared nbidders dummy_detailed final_ship_cost final_ship_cost_squared final_ship_cost_cubed auction_length dummy_sunday dummy_monday dummy_tuesday dummy_wednesday dummy_thursday dummy_friday constant The value for model 3 is equal to Once again, the estimates for model 3 are very similar to the earlier estimates except for the coefficients associated with the shipping cost because a new term has been added to the model, final_ship_cost_cubed. The important information from this model is the estimates related to the shipping cost. Theoretical equation (3) tells us that the _ _ _ _ implies that the estimated _ _ _ _ 2 _ _ 3 _ _, which _ _ _ _. Since 22

29 there is a cubed term in the model, the second derivative of the selling price with respect to shipping cost is also of interest. The theoretical second derivative is _ _ _ _ 2 6 _ _ which implies that the estimated second derivative is _ _ _ _ _ _. Below are the graphs of the first and second derivative with the shipping cost as the x variable: First Derivative Shipping Cost Figure 1: First Derivative 23

30 Second Derivative Shipping Cost Figure 2: Second Derivative The maximum of the first derivative corresponds to the x-intercept of the second derivative. This point is of interest because it tells us when the effect on the selling price caused by the shipping cost is the greatest. The maximum occurs when the shipping cost is $21.44, and this makes the first derivative equal to.155, implying that with an increase of shipping from $21.44 to $22.44, the final selling price will increase by $0.155, approximately and all else equal. However, this is not the optimal decision for the seller because the net effect on revenue must be taken into account. Below is the graph of the total revenue that a seller can expect with different shipping price levels, ceteris paribus. 24

31 Total Revenue Shipping Cost Figure 3: Total Revenue This graph shows where the optimal shipping price is from a seller s perspective. The equation is _ _ _ _ _ _ which is found by taking the original effect from the regression and adding the final shipping cost. This takes into account the shipping cost received by the seller and the effect to the final selling price caused by the shipping cost. The effect on the selling price eventually becomes a net effect of zero; this point is reached at $ Implying that at this shipping cost level the expected total price is the same as if the shipping cost were $0. The best decision is at the maximum of the total revenue graph or what maximizes the total selling price, which 25

32 includes the final selling or bidding price plus the shipping cost. According to the calculations, the optimal shipping amount is about $ This shipping cost corresponds to an expected total benefit of $34.72, which implies that the expected final selling price decreases by $9.31 with a shipping cost of $44.03, ceteris paribus. This particular shipping amount makes the first derivative equal to -1, which makes sense because when the first derivative equals -1, charging a $1 more has a net effect on revenue of $0. Also, if one takes any two points on the total revenue graph, the vertical distance represents the change in the expected total revenue caused by the difference in the shipping costs, all else equal. Of course, these estimates are based only on this model and my data that I collected, but the results are still interesting to discuss. Table 7: Model 4 Results Variable Coefficient Robust Std. Err. P-Value dummy_feedback_perfect dummy_detailed nbidders final_ship_cost final_ship_cost_squared final_ship_cost_cubed auction_length dummy_sunday dummy_monday dummy_tuesday dummy_wednesday dummy_thursday dummy_friday constant The value for model 4 is equal to The fourth model has a few interesting points to note. In the previous models the auction length coefficient was 26

33 positive but in this model it is negative which implies that using a one-day auction would be the best solution to the profit maximization problem. However, the coefficient does not have a strong economic or statistical significance with a p-value of and a coefficient of about Thus, most likely, the auction length does not play a huge factor in determining the final selling price of an auction. The reputation explanatory variable is a dummy variable that implies that the expected difference between a perfect feedback rating and any other rating is about $6.74, all else equal. The difference is quite significant and makes sense because the seller has not made a mistake. Detailed feedback in this model is much less significant than in the other models. The other coefficients are similar to the previous models. Table 8: Model 5 Results Variable Coefficient Robust Std. Err. P-Value dummy_feedback_perfect dummy_detailed int_detailed_perfect nbidders final_ship_cost final_ship_cost_squared final_ship_cost_cubed auction_length dummy_sunday dummy_monday dummy_tuesday dummy_wednesday dummy_thursday dummy_friday constant

34 The value for model 5 is equal to The fifth model adds in an interaction term between the perfect feedback dummy variable and the detailed feedback dummy variable. This adds a more interesting interpretation. There are four possible scenarios. The first scenario is if the perfect feedback and the detailed feedback dummy variables are equal to 1. This implies that the seller is experienced and has not made a mistake or received a negative feedback in the past twelve months. With this scenario the seller is expected to have the greatest selling price, all else equal. Thus, we will use it to compare to the other scenarios. The second scenario is if the seller has perfect feedback but is not an experienced seller with the expected difference in the selling price between scenario 1 and 2 to be about $0.89. The third scenario is if the seller does not have perfect feedback but is an experienced seller. This leads to an expected difference in the selling price between scenario 1 and 3 to be about $9.46. Finally, the fourth scenario is if the seller has neither perfect feedback nor much experience. The expected difference between scenario 1 and 4 is about $2.54. One could do any number of comparative expected differences but this shows how each scenario compares to one another. Perfect feedback seems to play a larger role in determining the price rather than experience does because the difference in those with perfect feedback and no experience is much smaller than for the sellers with experience and who do not have perfect feedback. One possible reason why an experienced seller without perfect feedback leads to significantly lower prices than those who are not experienced and without perfect feedback is because they have sustained a lower performance. In other words, those sellers have built a long-run negative reputation. Another possible explanation as to why reputation is more important 28

35 than experience is that the feedback rating is immediately recognizable from the auction listing. In order to notice if the seller has a detailed feedback rating one must click on the seller s feedback page. Thus, finding out the reputation of a seller is much easier than finding out the experience that the seller has through the detailed feedback rating. This scenario testing is the main finding from this model with the rest of the coefficients following a similar pattern as from model four. 29

36 CHAPTER FIVE CONCLUSION Throughout the different models, reputation, shipping price, and other interesting variables were analyzed to provide some insight into the consumer behavior in the online auction marketplace. We found that buyers did reward sellers that had a better reputation or a higher feedback percentage. The day of the week that an auction ends is important to sellers because certain days were associated with higher expected selling prices. An auction length did not seem to have a statistical or practical effect on the average selling price, which is important because obtaining the longest auction length has a fee. In order to understand the effects of the more interesting explanatory variables, we looked at models that used squared and cubed terms. This helped in understanding the dynamic of the different effects. In particular, we discussed this with the shipping price in great detail, which I think turned out to be the most interesting result from the data. The all or nothing dummy variable for reputation showed that perfect reputation is more valuable than experience is. The consumer tends to reward the reputation of a seller over the experience of a seller. This is more than likely due to the way in which this information is obtained by the consumer. We cannot make broad based statements from these analyses. However, based on this data, we can say that the analyses lend insight into the behavior of consumers in ebay s consumer electronics market. An area of research that can be explored further is how pictures play a role in consumers decision to bid. Not only if the auction has a picture but the quality of the pictures. Since picture quality is highly subjective it would 30

37 be difficult to objectively rate the pictures. However, if it can be done it should provide some more interesting findings from ebay. 31

38 APPENDIX: Empirical Data From ebay (excluding the auction end days) Final Selling Price Auction Length Number of Bidders Shipping Cost Feedback Percentage Dummy Detailed Dummy Perfect Feedback Interactive Detailed Perfect

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