Title: Intermarket Interactions and Efficiency in Electronic Markets: Eretailing Vs. Eauction Market
1Inter-market Interactions and Efficiency in
Electronic MarketsE-retailing Vs. E-auction
Market
- Byungtae Lee (with Kumar Mehta)
- Dept. of Information and Decision Sciences
- The University of Illinois at Chicago
2CRIM Funding Outcomes Thank You!
- An Empirical Evidence of New Economy IT
Investment and Labor Productivity for the Last 40
Years under review at Information Systems
Research - An Economic Model of Electronic Auction with
Information Asymmetry , under review at
Management Sciences - Inter-market Interactions and Efficiency in
Electronic MarketsE-retailing Vs. E-auction
Market, under review at Management Sciences
3Cybermics
- B2B EC IOS, EDI, SCM
- e-Payment States vs. Federal Government
- e-Auction vs. e-Reverse Auction
- E-Channel Yield Optimization
- E-Channel Management Click vs. Click-n-Mortar
- Digital Divides
- Surf or Ride?
- Build or Rent? Portal Service
4(No Transcript)
5Introduction
- In 2002, 129 billion Internet Auction
- 20 of transactions of 13B B2C eCmmerce in 1998
- B2C Auctions Constitute 33 of Auction Market
- Potentially very large market with growth
exceeding the retail
6Electronic Auction
- Traditional Auctions vs. Electronic Auctions
- Hard to price items vs. Readily available items
- Competition from other auctions and retailers
- Small number of bidders vs. Global market
- Short duration vs. flexible duration
- Access to information
- Scale of operations
7Introduction
- Rise of parallel market mechanisms
- Increasing Popularity of Internet Auctions
- Alternative to Internet Retailing
- Mode for Dynamic Pricing
- Claims and counter claims of bargain buys
8Auction vs. E-Auction
- Larger prospective customers (Lee, 1998, Klein et
al.) - The number of bidders becomes stochastic
- Larger perceived risk (Lack of Trust) (Spence et
al. 1970, Cox and Rich, 1964) - Smaller rate of returns in quality inspection
- Longer Bidding Time
- Multiple Items
- Lower costs for both seller and buyer
9Smart Use of E-Auction
- Multiplicity of mechanism for transactions
- Individual price discrimination
- Costless demand curve estimation
- Global garage sale
- Core Process for e-Procurement
10What Prices?
- What mechanism should we use?
- Overbidding in common-value auction?
- Issue of lemon market - informational asymmetry
11Issues
- Firms Strategy for Optimal Choice of Market
Mechanism Auction vs. Retail - Correction of Price Bias from Winners Curse
- Impact of multiplicity of market mechanisms
- Other Determinant of Bid Dispersion
12Price and Bid Dispersions I
13Price Dispersion and Market Efficiency
- Brynjolfsson and Smith (1999)
- Internet vs. Conventional Posted-Prices
- Internet posted-prices 6-7 lower
- Smith and Brynjolfsson (1999)
- Market heterogeneity, Branding, etc. as a source
of increased price dispersion - Lee (1997) Prices for used-cars on AUCNET higher
than conventional market - Clemons et al. (1998) Airline ticket prices vary
by as much as 20 - Seidman et al. (1999)
- E-Auction beat e-Retailing
14Price and Bid Dispersions II
15Why Over-bidding?
- Not Rational?
- Entertainment Values?
- Winners Curse?
- Search Costs on Cyberspace?
16Multiplicity of e-Mechanisms
- E vs. Bricks and Mortar
- E vs. e
- E and e Interact or Inform each other
- E-Mechanisms affects bidding dispersion beyond
firms differentiation strategies
17Informational Efficiency
- Stigler (1961)
- Homogenous goods, Rational consumers
- Price dispersion as a result of search costs
- Grossman and Stiglitz (1980)
- Arbitrage model, two types of agents
- Market failure when at extremes of informational
efficiency - More Rothschild (1973)
- Market with Incomplete Information
18Model
Auction Participants CAN observe retail markets!
19search
bid
Posted Price
Minimum Observed Prices
Auction Prices
20Search Cost Determinants
- Complexity of Product Description- Confidence on
getting what you want - Price Dispersion in Retail get smaller as product
information diffuses - Uncertainty Increase in Product Generation Change
- Search Efficiency of Surfers
- Expected Marginal Gain by Search
21Bidding Determinant
- Observed (or Belief) Price distribution
- Number of Participants
- Mix of Surfing Expertise
- Information Spill-over in the Market
22Theoretical Model
- Homogenous consumers
- Implications similar to Stigler (1961)
- Search will increasingly yield diminishing
benefit - Optimal searching marginal benefitsearch cost
- Frictionless ? Market Failure
- Implications for auction of posted-price goods
- Winning auction bids reveal degree of friction
- Revenue increasing with increased no. of bidders
- Increasing search cost beneficial for retailers
23Theoretical Model (continued)
- Heterogeneous consumers
- Experts (informed) vs. non-experts (unInformed)
- proportion of cost disadvantage a
- price uniformly distributed (0,1)
- optimal sampling by both
- Population mix l experts and 1-l non-experts
- Population size N
24Summary of Search Theory
- Different products have price dispersions
- Different product attracts informed and
uninformed customer distributions - Different auction mechanisms determine winning bid
25Hypothesis
- H1 Market Friction (Seemingly Winners curse)
exists - H2 Degree of Over-bidding decreases as Market of
The product matures - H3 Degree of Over-bidding decreases for More
Informed Customers - H4 Discontinued products will not over-bidding
26Data
- Brand New Computer Products (Scanners, Digital
Cameras, Drives, Printers, Motherboards) - Technical complexity used to categorize products
in expert/non-expert - Age New Release, Mature, Discontinued
- Winning Bids
- Time and Day of auction closing (High/Low
Traffic) - Auctions closing before or after 400 p.m.
- Auctions closing on weekday or weekend
- Retail prices for the exact same product on the
same day - Informed Customers Homogenous (experts) and
Heterogeneous (mix)
27Data Descriptive Statistics
- Total Observations 448 separate auctions
- Product Category
- Printers 157, Drives 68, Scanners 68
- Digital Cameras 117, Motherboards 38
- Product Type
- Non-expert 368, Expert 80
- Retail Price
- lt 200 238 200-400 99 400-600 79 gt 600
32 - Closing Time
- After 4 PM 177
- On Weekend 71
28Data Collection
- Bid Dispersion is collected from only ONE
e-Auction site - Price Dispersion was collected from multiple
sites with helps of Shopbots and PCA - At the same date with minimum delay
29Empirical Results
30Regression Results
Number of Observations 448 Adjusted
R-Square 0.761 F-Value 238.603
Coefficient Value t-statistic p-valu
e Marginal Return, -0.251
-10.508 0.000 Price Dispersion,
0.179 6.917 0.000 Information
Flow, -0.563
-22.120 0.000 Increased Traffic, ß_4pm 0.189
8.063 0.000 Presence of Non-Experts,
0.238 9.611 0.000 Proportion
of Non-Experts, 0.184
7.689 0.000 Preference between Channels 0.265
7.463 0.000
31Conclusions
- Search cost / Market friction, manifests in form
of Over-bidding in the auction market - E-auction is a very useful mechanism for sellers
- Periodically estimating demand curves for pricing
- Liquidation of inventory at the highest possible
price - Some bidders do get bargain buys
- E-auction market indicates that E-commerce is
largely efficient in terms of price discovery
32Implications for Auctioneer
- Timing closing of auctions
- Weekdays
- Weekends
- What mix of products?
- New Release, Mature, Liquidation
- High priced vs. low priced
- Bargain buys on liquidation items can serve
promotional purpose - Profit Margin or Fast Liquidation
33Future Work
- Bidder Data from Ubid
- Auction vs. Reverse Auction Comparison
- Demand Curve Bias Correction
34Cybermics
- B2B EC IOS, EDI, SCM
- e-Payment States vs. Federal Government
- e-Auction vs. e-Reverse Auction
- E-Channel Yield Optimization
- E-Channel Management Click vs. Click-n-Mortar
- Digital Divides
- Surf or Ride?
- Build or Rent? Portal Service
35E-Channel Decision?
- Using E-channels?
- Planned to add new E-channels?
- Or abandoned your experimental online channels?
- Provided Consulting regarding e-Channels?
- Email at blee_at_uic.edu or call me at 312 996-2847
36Thank You A Lot