Aiding Consumer Decisions on the Web Gary McClelland University of Colorado @ Boulder with...
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Transcript of Aiding Consumer Decisions on the Web Gary McClelland University of Colorado @ Boulder with...
Aiding Consumer Decisions on the Web
Gary McClelland
University of Colorado @ Boulder
with assistance from
Barbara Fasolo & Katharine Lange
Presented at The Wharton SchoolUniversity of Pennsylvania
26 February 2001
Today’s Tour
• Consumer-Aiding Websites• Winnowing via EBA, LEX, MCD,
WADD, MAUT, etc.• Why Attribute Correlations Matter• Typical Attribute Correlations• Effects of Attribute Correlations
Retail Sales 2000:4Q
• Online: Up 36% to $8.7 billion• Online: > 1.1 % of total retail• Total: Up 5.4%
Source: Reuters, 16 Feb 2001
Today’s Tour
• Consumer-Aiding Websites• Winnowing via EBA, LEX, MCD,
WADD, MAUT, etc.• Why Attribute Correlations Matter• Typical Attribute Correlations• Effects of Attribute Correlations
Winnowing Options
• Setting Attribute Cutoffs (EBA)• Sorting along Attributes
(LEX,TB)• Weighting Attributes (WADD)• Measuring Tradeoffs (MAUT)
Winnowing:LexicographicO1 O2 O3 O4 O5
A1 ++ + + + 0A2 – 0 + – ++A3 – + ++ + ++A4 – 0 –– + ++A5 – 0 0 + 0
LEX
Winnowing:Elimination by Aspects
O1 O2 O3 O4 O5
A1 ++ + + + 0A2 – 0 + – ++A3 – + ++ + ++A4 – 0 –– + ++A5 – 0 0 + 0
EBA
Winnowing:Satisficing
O1 O2 O3 O4 O5
A1 ++ + + + 0A2 – 0 + – ++A3 – + ++ + ++A4 – 0 –– + ++A5 – 0 0 + 0
SAT
Winnowing: Most Confirming
DimensionsO1 O2 O3 O4 O5
A1 ++ + + + 0A2 – 0 + – ++A3 – + ++ + ++A4 – 0 –– + ++A5 – 0 0 + 0
MCD
Winnowing:Adding (Equal Wts)
O1 O2 O3 O4 O5
A1 ++ + + + 0A2 – 0 + – ++A3 – + ++ + ++A4 – 0 –– + ++A5 – 0 0 + 0
ADD
Winnowing:ImplicationsO1 O2 O3 O4 O5
A1 ++ + + + 0A2 – 0 + – ++A3 – + ++ + ++A4 – 0 –– + ++A5 – 0 0 + 0
LEX SAT EBA MCD ADD
Attribute Processing
Opt 1 Opt 2 Opt 3
Att A VA1 VA2 VA3
Att B VB1 VB2 VB3
Att C VC1 VC2 VC3
EBA orLEX orTakeBest
Option Processing
Opt 1 Opt 2 Opt 3
Att A VA1 VA2 VA3
Att B VB1 VB2 VB3
Att C VC1 VC2 VC3
WADD or MAUT Score
Today’s Tour
• Consumer-Aiding Websites• Winnowing via EBA, LEX, MCD,
WADD, MAUT, etc.• Why Attribute Correlations
Matter• Typical Attribute Correlations• Effects of Attribute Correlations
Equal Weights History
• Wilks (1938)• Gulliksen (1950)• Dawes & Corrigan (1974)• Einhorn & Hogarth (1975)• Wainer (1976)• Meehl (1999)
Today’s Tour
• Consumer-Aiding Websites• Winnowing via EBA, LEX, MCD,
WADD, MAUT, etc.• Why Attribute Correlations Matter• Typical Attribute Correlations• Effects of Attribute Correlations
Real Choice Sets
• What are their attribute correlations?
• Are they approximately nondominated sets?
• Consumer Reports
-P/Q Correlations from CR
-.17 Air Conditioners
-.27 Bike Helmets
+.16 Dishwashers
-.82 Mtn Bikes
-.74 Printers
-.49 Pro Ranges
-.28 Fridge
-.59 27” TVs
-.16 Vacuums
-.56 Wall Oven
Attribute Correlations from CR
+.35 Air Conditioners
+.07 Bike Helmets
-.03 Dishwashers
+.37 Mtn Bikes
+.18 Printers
+.47 Pro Ranges
-.06 Fridge
+.05 27” TVs
+.12 Vacuums
+.04 Wall Oven
Average r = –.05
O1 O2 O3 O4 O5
A1 ++ + + + 0A2 – 0 + – ++A3 – + ++ + ++A4 – 0 –– + ++A5 – 0 0 + 0
LEX SAT EBA MCD ADD
Today’s Tour
• Consumer-Aiding Websites• Winnowing via EBA, LEX, MCD,
WADD, MAUT, etc.• Why Attribute Correlations Matter• Typical Attribute Correlations• Effects of Attribute
Correlations
Sample Data Streams
Att Opt Time0 1 276
0 0 15951 0 8510 0 8590 1 8360 0 5350 1 9750 2 6520 3 6520 4 543
Att Opt Time1 4 5571 3 1660 2 2341 2 4721 1 765
2 1 2111 2 2 2519 2 3 10312 4 448
Attribute Focus
WebIDB cf. MouseLab
• WebIDB replicated MouseLab results– Attribute Focus is the default strategy– Increasing Attributes -> Attribute Focus– Increasing Options -> Less info, more var.
• Different result– Somewhat more information viewed in
WebIDB– Somewhat greater attribute focus in
WebIDB
Prior Research on Correlation Effects
• Johnson, Meyer & Ghose (1989)
• Theory: Negative –> Attribute-based
• Results: Null
• Bettman, Johnson, Luce & Payne (1993)
• Theory: Negative –> Option-based
• Results: Negative –> Option-based
Experiments
• Study 1– 8 att x 5 opts– Attribute
Correlation: Pos (.5) vs. Neg (-.14)
– 8 Matrices– Between Subjects
• Study 2– Within Subjects:
Switch after 4 Matrices
Results Summary
• Default Strategy is Attribute Processing
• Negative Correlation —> Option Processing
• Immediate Sensitivity to Correlation• Quickly Switch to Option Processing• Amount of Information Constant
Research Questions
• What winnowing strategies do consumers use?
• Attribute-based unless forced towards Option-based by negative attribute correlations
Research Questions
• What winnowing strategies might consumers be willing to use if aided?
• And how do attribute correlations affect the use of such aids?