Application of Warranting Theory to Online Third Party Marketplaces
•Download as PPTX, PDF•
0 likes•67 views
Research on the factors that contribute to price premiums in eBay auctions. Published in the ACM 2016 Proceedings of Social Media & Society.
Recommended
Recommended
More Related Content
Recently uploaded
Recently uploaded (20)
Featured
Featured (20)
Application of Warranting Theory to Online Third Party Marketplaces
- 1. L O U I S V I L L E . E D U Testing the Application of Warranting Theory to Online Third Party Marketplaces: The Effects of Information Uniqueness and Product Type W. Scott Sanders Department of Communication College of Arts & Sciences Gopi Chand Nutakki Knowledge Discovery & Web Mining Lab Dept. of Computer Engineering & Computer Science Speed School of Engineering Olfa Nasraoui Knowledge Discovery & Web Mining Lab Dept. of Computer Engineering & Computer Science Speed School of Engineering
- 2. L O U I S V I L L E . E D U Rationale: Why do we care about eBay? Internet third-party marketplaces are examples of two-sided markets that match users and consumers to sellers and products. Problem: Participants must evaluate risk when: 1. Seller is unknown 2. Product quality cannot be directly assessed.
- 3. L O U I S V I L L E . E D U Background: Warranting Theory Warrant is the certainty in judgment produced by informational cues that authenticate identity or attributes. The weight given to informational cues when forming impressions is derived from the extent to which the cues are perceived as being immune from manipulation or fabrication by self-interested parties.
- 4. L O U I S V I L L E . E D U Background: Warranting Theory Warrant is the certainty in judgment produced by informational cues that authenticate identity or attributes. The weight given to informational cues when forming impressions is derived from the extent to which the cues are perceived as being immune from manipulation or fabrication by self-interested parties. H1: A seller’s feedback score will be negatively associated with price discount such that more reputable sellers will sell comparable items at a higher price than less reputable sellers.
- 5. L O U I S V I L L E . E D U Constraints that limit the production of misleading information in online environments can function as warranting cues. The increased effort and time required to produce individualized photos and descriptions gives them increased warranting value as opposed to stock photos and boilerplate descriptions. H2: Auction listings with vendor created photographs of items will have a lower price discount than auction listings which use stock photographs. H3: There will be a positive association between the frequency with which an image is used and price discount such that listings which display commonly used product images will have lower selling prices. H4: There will be a negative association between the uniqueness of item descriptions and price discount such that listings with more unique item descriptions should have higher selling prices. x 145 x 198 X 21 StockIndividualized Individualized
- 6. L O U I S V I L L E . E D U Constraints that limit the production of misleading information in online environments can function as warranting cues. The increased effort and time required to produce individualized photos and descriptions gives them increased warranting value as opposed to stock photos and boilerplate descriptions. H2: Auction listings with vendor created photographs of items will have a lower price discount than auction listings which use stock photographs. H3: There will be a positive association between the frequency with which an image is used and price discount such that listings which display commonly used product images will have lower selling prices.
- 7. L O U I S V I L L E . E D U Constraints that limit the production of misleading information in online environments can function as warranting cues. The increased effort and time required to produce individualized photos and descriptions gives them increased warranting value as opposed to stock photos and boilerplate descriptions. H4: There will be a negative association between the uniqueness of item descriptions and price discount such that listings with more unique item descriptions should have higher selling prices.
- 8. L O U I S V I L L E . E D U Background: Search & Experience Goods Attributes of experience goods can only be determined through use or sampling. Attributes of search goods are easily determined prior to purchase or use.
- 9. L O U I S V I L L E . E D U Background: The Internet’s Impact on Search Goods The Internet makes it easier to determine the relevant attributes of search goods. This results in increased competition and depressed sale prices.
- 10. L O U I S V I L L E . E D U Background: The Internet’s Impact on Search Goods The Internet makes it easier to determine the relevant attributes of search goods. This results in increased competition and depressed sale prices. H5: Auction listings for search goods will have a higher price discount that auction listings for experience goods.
- 11. L O U I S V I L L E . E D U Rationale: Interactions The assumption of warranting theory is that information is uncertainty reducing to the extent to which it is judged accurate. However, informational cues, such as photos or descriptions, may be wholly accurate and yet have limited diagnostic value about the relevant traits of experience goods. H6a: There is an interaction between good type and photo type such that search goods with individualized photographs will exhibit a higher price discount than search goods with stock photos or experience goods.
- 12. L O U I S V I L L E . E D U Rationale: Interactions The assumption of warranting theory is that information is uncertainty reducing to the extent to which it is judged accurate. However, informational cues, such as photos or descriptions, may be wholly accurate and yet have limited diagnostic value about the relevant traits of experience goods. H6b: There is an interaction between good type and seller reputation such that increased seller reputation will result in lower price discounts (i.e. higher prices) for experience goods.
- 13. L O U I S V I L L E . E D U Collected from eBay finding and shopping API’s. Used eBay’s referenceID which denotes identical products. Dataset was manually screened for: • Misidentified products • Altered products • “Parts only” listings • Special editions • Autographed products Methods: Data Collection Search Goods
- 14. L O U I S V I L L E . E D U Collected from eBay finding and shopping API’s. Used eBay’s referenceID which denotes identical products. Dataset was manually screened for: • Misidentified products • Altered products • “Parts only” listings • Special editions • Autographed products Methods: Data Collection Experience Goods
- 15. L O U I S V I L L E . E D U Methods: Measures – Image Uniqueness & Image Type Image type was coded as : • Stock Image • Individualized Photo Individualized Photo Stock Image
- 16. L O U I S V I L L E . E D U Methods: Measures – Image Uniqueness & Image Type Image uniqueness was how many times the image recurred in the dataset. 1. Images were greyscaled. 2. Images are reduced to a 16 X 16 pixelated image. 3. Average light and dark value is taken across the image. 4. The difference between the average light and dark value and value of each pixel is taken to create a unique vector representing the image (e.g. fingerprint). Original Image Greyscale 16 X 16 Average hash process (Not to Scale)
- 17. L O U I S V I L L E . E D U Methods: Measures – Description Uniqueness Item 1 Item 2 Item 3 … Item 1 - Score Score … Item 2 Score - … Item 3 Score Score - … … … … … - μ μ μ Descriptions were compared by cosine similarity. Cosine similarity compares two listings as vectors representing the frequency of terms within a document. Scores are averaged to create a description uniqueness score. Matrix of Cosine Similarity Scores for a Products Listings
- 18. L O U I S V I L L E . E D U Methods: Measures – Price Discount Product MSRP Avengers DVD 17.95 Bose Quiet Comfort 15 299.95 Coco Mademoiselle 122.00 50 Shades of Grey (Trilogy) 47.85 Windows 7 Professional 133.99 GTA 5 – Xbox 360 59.99 Raybans RB2140 155.00 WD Blue HDD 54.99 Products were not directly comparable due to differing prices. Therefore, price discount was calculated for each item listing as: Price Discount = (𝑆𝑎𝑙𝑒 𝑃𝑟𝑖𝑐𝑒 −𝑀𝑆𝑅𝑃) 𝑀𝑆𝑅𝑃 * -1
- 19. L O U I S V I L L E . E D U Multiple Regression Analysis Predicting Percent Price Discount Variables B SE B β Intercept .45 .03 Image Frequency .80 .07* .30 Description Uniqueness .27 .03* .15 Seller Feedback Score (Log) -.02 .01* -.11 Good Type (Exp. = 0) .23 .01* .60 Photo Type (Ind. = 0) .04 .01* .08 Photo X Good Type -.10 .02* -.20 Feedback X Good Type .07 .01* .30 * p < .001; R=.68; R2=46 Hypothesis Supported? H1: Higher Reputation -> Lower Discount H2: Individualized Photos -> Lower Discount H3: Higher Image Freq. -> Higher Discount H4: Unique Description -> Lower Discount H5: Search Goods -> Higher Discount H6a: Photo Type X Good Type H6b: Good Type X Reputation
- 20. L O U I S V I L L E . E D U Multiple Regression Analysis Predicting Percent Price Discount Variables B SE B β Intercept .45 .03 Image Frequency .80 .07* .30 Description Uniqueness .27 .03* .15 Seller Feedback Score (Log) -.02 .01* -.11 Good Type (Exp. = 0) .23 .01* .60 Photo Type (Ind. = 0) .04 .01* .08 Photo X Good Type -.10 .02* -.20 Feedback X Good Type .07 .01* .30 * p < .001; R=.68; R2=46 Hypothesis Supported? H1: Higher Reputation -> Lower Discount H2: Individualized Photos -> Lower Discount H3: Higher Image Freq. -> Higher Discount H4: Unique Description -> Lower Discount H5: Search Goods -> Higher Discount H6a: Photo Type X Good Type H6b: Good Type X Reputation Both warranting cues significantly predict price discounts.
- 21. L O U I S V I L L E . E D U Multiple Regression Analysis Predicting Percent Price Discount Variables B SE B β Intercept .45 .03 Image Frequency .80 .07* .30 Description Uniqueness .27 .03* .15 Seller Feedback Score (Log) -.02 .01* -.11 Good Type (Exp. = 0) .23 .01* .60 Photo Type (Ind. = 0) .04 .01* .08 Photo X Good Type -.10 .02* -.20 Feedback X Good Type .07 .01* .30 * p < .001; R=.68; R2=46 Hypothesis Supported? H1: Higher Reputation -> Lower Discount H2: Individualized Photos -> Lower Discount H3: Higher Image Freq. -> Higher Discount H4: Unique Description -> Lower Discount H5: Search Goods -> Higher Discount H6a: Photo Type X Good Type H6b: Good Type X Reputation More frequent images lead to higher discounts. More unique descriptions lead to higher discounts.
- 22. L O U I S V I L L E . E D U Multiple Regression Analysis Predicting Percent Price Discount Variables B SE B β Intercept .45 .03 Image Frequency .80 .07* .30 Description Uniqueness .27 .03* .15 Seller Feedback Score (Log) -.02 .01* -.11 Good Type (Exp. = 0) .23 .01* .60 Photo Type (Ind. = 0) .04 .01* .08 Photo X Good Type -.10 .02* -.20 Feedback X Good Type .07 .01* .30 * p < .001; R=.68; R2=46 Hypothesis Supported? H1: Higher Reputation -> Lower Discount H2: Vendor Created Photos -> Lower Discount H3: Higher Image Freq. -> Higher Discount H4: Unique Description -> Lower Discount H5: Search Goods -> Higher Discount H6a: Photo Type X Good Type H6b: Good Type X Reputation Search goods are more highly discounted than experience goods.
- 23. L O U I S V I L L E . E D U Multiple Regression Analysis Predicting Percent Price Discount Variables B SE B β Intercept .45 .03 Image Frequency .80 .07* .30 Description Uniqueness .27 .03* .15 Seller Feedback Score (Log) -.02 .01* -.11 Good Type (Exp. = 0) .23 .01* .60 Photo Type (Ind. = 0) .04 .01* .08 Photo X Good Type -.10 .02* -.20 Feedback X Good Type .07 .01* .30 * p < .001; R=.68; R2=46 Hypothesis Supported? H1: Higher Reputation -> Lower Discount H2: Vendor Created Photos -> Lower Discount H3: Higher Image Freq. -> Higher Discount H4: Unique Description -> Lower Discount H5: Search Goods -> Higher Discount H6a: Photo Type X Good Type H6b: Good Type X Reputation Warranting cues interact with good type. Suggests that different good types are assessed using different criteria online.
- 24. L O U I S V I L L E . E D U Results: Good Type X Photo Type Individualized photos leads to lower prices on search goods, perhaps to due to increased competition for undifferentiated goods. Photo TypePriceDiscount
- 25. L O U I S V I L L E . E D U Results: Good Type X Reputation Higher reputation leads to increased prices for experience goods by reducing seller uncertainty. Increased discount for search goods may be a confound (i.e. reputation and sales volume are linked on eBay!). PriceDiscount Seller Feedback (log)
- 26. L O U I S V I L L E . E D U Implications Results confirm warranting theory predictions: 1. Both photo type and reputation are significant. 2. Contributions to the model reflect their difficulty in being manipulated. Warranting cues differentially effect price based on good type: 1. Individualized photos leads to lower prices on search goods. 2. Reputation most strongly benefits experience goods.
- 27. L O U I S V I L L E . E D U Practical Implications Expending effort to individualize the text descriptions found in listings may be misplaced. Creating individualized images for a listing is generally recommendable although the seller must carefully consider the type of item they are listing. When image reuse is detected it may undermine the warranting value of the informational cue.
- 28. L O U I S V I L L E . E D U Thank You! Contact Information: W. Scott Sanders Twitter: @wscottsanders Email: scottsanders@louisville.edu
Editor's Notes
- Internet third-party marketplaces are examples of two-sided markets that match users and consumers to sellers and products. Ebay is a well established example but many emerging forms of internet commerce are actually two-sided markets. For example, ride sharing services such as uber and lyft depend upon matching drivers to riders. Likewise, Airbnb matches hosts to travelers. The problem that these Internet platforms faces is that they must establish trust when 1) the seller is unknown and 2) the product or service can not be directly assessed prior to use. Therefore, there are two routes to uncertainty reduction for the party – you may provide afforadances that either 1) increase confidence that the seller or service provider will perform as promised or 2) Allows for the assessment of the good or service. In short, platform success depends not only upon increased convenience and the provision of lower cost goods and services, but also on the affordances of the platform that reduce uncertainty for both parties.
- One theory that focuses on how different sources of information are weighed in the attribution process is warranting theory. Warrant has traditionally described the confidence and certainty in judgements produced when linking online and offline self-presentations in interpersonal contexts. The extent to which information linked your online self-presentation to your offline identity was held to be warranting. More recently warrant has come be considered simply certainty in judgment produced by informational cues that authenticate identity or attributes in an online environment. The weight given to informational cues when forming impressions is derived from the extent to which the cues are perceived as being immune from manipulation or fabrication by self-interested parties.
- For example, information in reputation systems found on many online platforms such as eBay, Yelp, or AirBnb is warranting because it would be relatively difficult to manufacture or manipulate as compared to statements made about oneself. Thus, reputational information and reviews, which are produced by third parties, should be trusted over statements made by the individual or vendor to which the information refers. Therefore, we hypothesize:
- General rule of warranting theory is that anything that produces a constraint on the production of an informational cue, whether via social sanctions, anticipated future interaction, or cost of the cues production, increases its warranting value. The perfect warranting cue is one that cannot be manipulated or faked so that the audience knows that the information is true. Warranting theory suggests that constraints that limit the production of misleading information in online environments can function as warranting cues. One such constraint is the cost, in both time and resources, to produce information. Both photos and descriptions are produced by the seller, a self-interested party, and should be attributed less weight in the attribution process than third party warranting information. However, warranting theory suggests that given the increased effort and time required to produce individualized photos and descriptions, they served as more reliable cues and have increased warranting value, as opposed to stock photos and boilerplate descriptions. Likewise, because value of individualized pictures and descriptions is that they are thought to be both more costly to produce and more informative about the item the buyer will receive, when an image or description is reused we would expect it to undermine the warranting value of that information.
- H2: There will be a positive association between the frequency with which an image is used and price discount such that listings which display commonly used product images will have lower selling prices. H3: There will be a negative association between the uniqueness of item descriptions and price discount such that listings with more unique item descriptions should have higher selling prices.
- H4: Auction listings with vendor created photographs of items will have a lower price discount than auction listings which use stock photographs.
- Although researchers have previously attempted to apply warranting theory to online commerce, they failed to acknowledge that not all goods are the same. Search goods are those items whose attributes are easily determined prior to usage. For example, the speed and capacity of an SD card are it’s defining attributes and can be easily determined prior to purchase. Likewise, the size of a tool, whether it is metric or imperial, and the materials it is made of can be detected prior to purchase. In turn, attributes of experience goods can only determined via use or sampling. No one can convey the exact taste of a wine or how a cosmetics color will look and feel on your skin. . These differences have import implications for third-party internet marketplaces;
- First, search goods are more subject to price competition due to their ease of evaluation. For example, this listing of SSD’s list their capacity and speed in a table next to one anther making comparison incredibly easy.
- Second, good type may determine what types of informational cues are most relevant in online contexts to reducing uncertainty about a product. The assumption of warranting theory is that information is uncertainty reducing to the extent to which it is judged accurate. However, with experience goods, item descriptions and photographs may be entirely accurate and yet still inadequate to convey diagnostic information regarding the products quality. We would expect good type to interact with photo type as it has the potential to be diagnostic for search goods but not for experience goods. Likewise, reputational information should be more salient for experience goods, as opposed to search goods, as it is one of the few cues that can reduce uncertainty albeit indirectly by building confidence in the vendor.
- Second, good type may determine what types of informational cues are most relevant in online contexts to reducing uncertainty about a product. The assumption of warranting theory is that information is uncertainty reducing to the extent to which it is judged accurate. However, with experience goods, item descriptions and photographs may be entirely accurate and yet still inadequate to convey diagnostic information regarding the products quality. We would expect good type to interact with photo type as it has the potential to be diagnostic for search goods but not for experience goods. Likewise, reputational information should be more salient for experience goods, as opposed to search goods, as it is one of the few cues that can reduce uncertainty albeit indirectly by building confidence in the vendor.
- Completed auctions for each product were selected the finding and shopping ebay API’s using the referenceID which denotes identical products. Listings were screened to identify any elements of the listing that might effect the ultimate sale price. We used the following items to represent experience and search goods:
- Completed auctions for each product were selected the finding and shopping ebay API’s using the referenceID which denotes identical products. Listings were screened to identify any elements of the listing that might effect the ultimate sale price. We used the following items to represent experience and search goods:
- Individualized photos are coded by hand based upon whether it represented a stock promotional image.
- Image uniqueness was determined by fingerprinting images using an average hash algorithm:
- Cosine similiarity compares to vectors of values representing a terms frequency in a document (in this case the listing description) so that we get a score how similar each listing is to every other listing. These scores are arranged in a matrix. Similiarity scores can also, when considered in mass, give us some idea of how unique an item description is. Thus, the average cosine similiarity score was taken for each listing when comparing it to all other listings for the same product.
- Different product types naturally cost different amounts. So that we could compare apples to apples, we took the MSRP from publisher and manufacturer websites and converted the price an item was sold for on ebay to price discount scores using the above formula. This created an equal metric so that items could be directly compared.
- Both warranting cues significantly predicted price discounts. As would be expected by warranting theory, reputational information, which is more difficult to manipulate given it’s third party nature, is more influential than vendor provided photos.
- Less unique photos leads to higher discounts (i.e. lower sale prices). Contrary to expectations more unique descriptions leads to higher discounts (potentially less relevant – this is an area for future exploration).
- Search goods are more highly discounted than experience goods potentially due to competition and the ease of assessing quality/ relevant attributes.
- Individualized photos leads to lower prices on search goods, perhaps to due to increased competition for undifferentiated goods. Individualized photos allow buyers to easily distinguish the quality of the items and may force sellers of identical items to compete on price.
- Higher reputation leads to increased prices for experience goods by reducing seller uncertainty. This result bears further exploration but it’s interesting. -
- This study examined two warranting cues, photo type, which is easy for the vendor to manipulate, and reputation, which is much more difficult without a concerted effort. Results confirm warranting theory predictions: Both were significant Reputation, which is harder for individual to manipulate than photo type, proved to be more influential. The primary contribution of this paper is that it specifies an important moderator, good type, to the application of warranting theory. Specifically, the degree to which warranting cues are ultimately uncertainty reducing depends upon whether the item being sold represents a search or an experience good. Reputational gains for sellers of experience goods online should correspond to increased prices as it indirectly affects the evaluation of the product via judgments of the seller. Photo type, in turn, effects search goods. Individualized photos allow buyers to easily distinguish the quality of the items and may force sellers of identical items to compete on price. On eBay the value of individualized images and descriptions of items for sale is that they are presumed by the buyer to represent the actual item that they will receive when image reuse is detected it may undermine the warranting value of the informational cue. The results of the present study show that images which appeared more frequently in the dataset corresponded to lower sale prices for comparable items Users may compare multiple listings for both price and quality when shopping in marketplace which contains used goods and the results suggests that when buyers detect reused images they do not consider them as uncertainty reducing as unique images. unique text descriptions of items for sale result in lower rather than higher sale prices. Text descriptions by their nature should have relatively low warranting value as there are little constraints on what a person can choose to say about an item for sale. idiosyncratic text descriptions may contain less relevant information for assessing product quality, it may be that buyers favor more standardized, boilerplate descriptions
- Expending effort to individualize the text descriptions found in listings may be misplaced. Rather, results suggest it is recommendable to find detailed and well written descriptions from other sources rather than attempting to describe the unique history of each item. Part of the problem may be one of informativeness – future work could consider the role of Gricean maxims for communication quality on listings. Second, creating individualized images for a listing is generally recommendable although the seller must carefully consider the type of item they are listing. The use of unique, individualized images appears to result in higher prices for experience goods whereas individualized images for search goods leads to lower prices. In sum, this study highlights not only how the importance of informational cues within a product listing varies based upon good type, but also underlines the fact that research shouldn’t treat product listings as unrelated instances. Clearly, consumers approach listings contextualized within a larger marketplace and are comparing across listings. This study provides evidence that this effects the price paid for items and should be considered an important facture in future research.