e-loyalty networks in online auctions
Post on 08-Jan-2016
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INBAL YAHAV WOLFGANG JANK
R.H. SMITH SCHOOL OF BUSINESS, UNIVERSITY OF MARYLAND
E-Loyalty Networks in Online Auctions
Motivation
Sellers Bidders
Objective High profit High conversion rate
Get the product? Low price?
Get the product Get the product (quality)
Means Trust Feedback score Lit
Auction design (e.g., open price, duration, etc.)
IS THAT ENOUGH??
Actors
Research Questions
1. How to define and measure e-loyalty?
3. What factors drive loyalty in online auctions?
2. How does loyalty impact auction outcome (price, conversion)?
Data
~350 Sellers~700 Repeating Buyers
Loyalty in the Literature
Definition: repeating purchases
Brand-switch literature: Probability of switching to another brand Distribution of purchases across different brands
(commonly 2 brands)
Research Questions
1. How to define and measure e-loyalty?
3. What factors drive loyalty in online auctions?
2. How does loyalty impact auction outcome (price, conversion)?
Define and Measure eLoyalty
Three steps measurements
Construct eLoyalty network Transform network into loyalty distribution Transform the distribution into quantifiers using PC
analysis
Define and Measure eLoyalty
eLoyalty networks
Bipartite graph with: First nodes set: sellers (red) Second node set: buyers (white)
Arcs: purchases, with the width corresponding to the number of interactions
Define and Measure eLoyalty
eLoyalty disribution
SellersBuyers
100%
100%
100%
70%
80%
2. Measure the perceived loyalty per seller (~distribution of the weighted in-degree)
1. Measure proportion of interactions per buyer (~normalized distribution of out-degree)
30%
Define and Measure eLoyalty
Transform the distribution into two quantifiers (PC1, PC2) that measure the difference between the sellers’ perceived loyalty.
m sellers
(discrete grid)
First & Second PCA Scores
(~80% of the variation)
InputPCA
Sellers’ Perceived eLoyalty: PCAs
Most weight on medium-scores PC2 contrasts the moderate loyalty distributionfrom the extremes – distinguishes sellers that have neither extremely loyal norextremely disloyal bidders
Very little weight on low scores , very large weight on high scores (between 0.8 and 1 PC1 contrasts distributions of sellers with extremely loyal bidders with those that are little loyal
Research Questions
1. How to define and measure e-loyalty?
3. What factors drive loyalty in online auctions?
2. How does loyalty impact auction outcome (price, conversion)?
Modeling eLoyalty : Effect of eLoyalty on Price
OLS/ WLS regression High volume sellers have multiple, inter-dependent
auctions Low-volume sellers have only few auctions Violates regression assumption
Modeling eLoyalty : Effect of eLoyalty on Price
Random-effects regression model Account for seller-specific variation
Heteroscedasticity
Modeling eLoyalty : Effect of eLoyalty on Price
Segment sellers into three groups
Modeling eLoyalty : Effect of eLoyalty on Price
Segment sellers into three groups: model fit
Low volume Medium volume High volume
R2=0.81 R2=0.77 R2=0.83
Effect of eLoyalty on Price
The effect of loyalty depends strongly on size of the seller: High volume sellers can extract huge price-premiums from loyal
bidders The impact of loyalty is much smaller for sellers of smaller scale
Coefficient Medium volume Low volume High volume(Intercept) -0.21 0 4StartPrice 0.08 0.04 0.05AuctionDuration 0 0 0log(ItemQuantity) 0.12 0.14 0.08Bidcount 0.11 0.13 0.07log(Pieces) 0.19 0.08 0.36Size 0.03 0.03 0.07log(SellerFeedback) 0.04 0.04 0.12log(Volume) -0.04 0.01 -0.81PC1 0.21 -0.24 2.74PC2 0.02 -1.72 -15.41
Summary
Define and measure eLoyalty eLoyalty network Buyers loyalty ~ normalized distribution of out-degree Seller perceived loyalty ~ distribution of the weighted
in-degree Transform the distribution into quantifiers using PC
analysis
Modeling eLoyalty: data segmentationConclusions
Loyalty has higher impact on high volume sellers Saturated market
Discussion
The analysis can be replicated to other products; the results might change
Temporal networks Examine the evaluation of eLoyalty We did not observe temporal effect in our data
More Information?
Inbal Yahaviyahav@rhsmith.umd.edu
http://www.rhsmith.umd.edu/faculty/phd/inbal/
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