Intermarket Interactions and Efficiency in Electronic Markets: Eretailing Vs. Eauction Market - PowerPoint PPT Presentation

1 / 36
About This Presentation
Title:

Intermarket Interactions and Efficiency in Electronic Markets: Eretailing Vs. Eauction Market

Description:

Lee (1997): Prices for used-cars on AUCNET higher than conventional market ... Or abandoned your experimental online channels? Provided Consulting regarding ... – PowerPoint PPT presentation

Number of Views:79
Avg rating:3.0/5.0
Slides: 37
Provided by: kumar5
Learn more at: http://www.uic.edu
Category:

less

Transcript and Presenter's Notes

Title: Intermarket Interactions and Efficiency in Electronic Markets: Eretailing Vs. Eauction Market


1
Inter-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

2
CRIM 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

3
Cybermics
  • 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)
5
Introduction
  • 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

6
Electronic 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

7
Introduction
  • 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

8
Auction 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

9
Smart Use of E-Auction
  • Multiplicity of mechanism for transactions
  • Individual price discrimination
  • Costless demand curve estimation
  • Global garage sale
  • Core Process for e-Procurement

10
What Prices?
  • What mechanism should we use?
  • Overbidding in common-value auction?
  • Issue of lemon market - informational asymmetry

11
Issues
  • 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

12
Price and Bid Dispersions I
13
Price 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

14
Price and Bid Dispersions II
15
Why Over-bidding?
  • Not Rational?
  • Entertainment Values?
  • Winners Curse?
  • Search Costs on Cyberspace?

16
Multiplicity 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

17
Informational 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

18
Model
Auction Participants CAN observe retail markets!
19
search
bid
Posted Price
Minimum Observed Prices
Auction Prices
20
Search 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

21
Bidding Determinant
  • Observed (or Belief) Price distribution
  • Number of Participants
  • Mix of Surfing Expertise
  • Information Spill-over in the Market

22
Theoretical 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

23
Theoretical 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

24
Summary of Search Theory
  • Different products have price dispersions
  • Different product attracts informed and
    uninformed customer distributions
  • Different auction mechanisms determine winning bid

25
Hypothesis
  • 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

26
Data
  • 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)

27
Data 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

28
Data 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

29
Empirical Results
30
Regression 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
31
Conclusions
  • 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

32
Implications 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

33
Future Work
  • Bidder Data from Ubid
  • Auction vs. Reverse Auction Comparison
  • Demand Curve Bias Correction

34
Cybermics
  • 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

35
E-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

36
Thank You A Lot
Write a Comment
User Comments (0)
About PowerShow.com