why are prices of the same item not the same at my.com...

Why are Prices of the Same Item not Why are Prices of the Same Item not the Same at My.com & Your.com?:the Same at My.com & Your.com?:Drivers of eDrivers of e--tailer Price Dispersiontailer Price Dispersion

Xing Pan, Brian Ratchford and Venky Shankar

R.H. Smith School of Business

University of MarylandPresented at

eBusiness@MIT

October 3, 2001

10/3/01 Copyright Pan, Ratchford, and Shankar. All rights reserved. 2

Agendaw Introduction to price dispersion

w Prior research

w Research questions and contributions

w Data

w Analysis

w Results & Discussion

w Managerial implications

w Limitations & Future research


Introduction to Price Dispersion

w The failure of the “law of one price” has been widely observed.

w Price dispersion found for even homogeneous products in environments conducive to perfect competition (Sorensen 2000)


Prior Research: Theoreticalw Incomplete information and costly search

(Stigler 1961)w Price discrimination

(Miller 1993; Clemons, et al. 1998)w Inertial brand loyalty, resulting from time lags in

awareness (Wernerfelt 1991)w Staggered price setting due to menu cost

(Fishman 1992)w Demand uncertainty, costly capacity and price presetting

(Dana 1999)w Heterogeneity in product & seller attributes

(Sorensen 2000; Brynjolfsson and Smith 2000)


Frictionless e-Commerce(Smaller Price Dispersion among e-tailers)

w More information available online

w Online shoppers search more extensively

w Competing offer is a click away

w Easy online entry discourages high prices

w Cost transparency

w Highly frequent and small price adjustments


Prior Research Online: Empiricalw Price dispersion online not smaller than offline, and

the pattern is found to be persistent (Bailey 1998; Brynjolfsson and Smith 2000; Smith et al. 2000)

w Greater information online leads to lower price sensitivity and wider range of prices (Shankar et al. 2001)

w Sellers with the lowest prices never have the highest market shares as search models predict (Carlson and McAfee 1983)

w Heterogeneity in product & seller attributes ?(Brynjolfsson and Smith 2000)


Drivers of Price Dispersion Online

w e-tailer characteristics– Shopping convenience – Reliability– Product information– Shipping and handling– Pricing policy– Time of market entry– Trust or Third party certification/recommendation– Consumer awareness (Web traffic, External links)


Drivers of Price Dispersion Online

wMarket characteristics– Number of competitors in market – nonlinear

– Involvement (Price level)

– Product popularity

w Category Characteristics– Category uniqueness


Research Questions

wWhat are the drivers of price dispersion online?

w How much of price dispersion online is explained by category vs. e-tailer vs. market characteristics?


Contributionsw First to propose a comprehensive

framework and set of determinants of price dispersion online and empirically test them.w Go beyond e-tailer characteristics to study

the impact of market characteristics on price dispersion online.w Empirically generalizable: 8 categories,

6739 prices, 105 e-tailers.


Dataw Bizrate.com

– Prices– e-tailer ratings on 10 attributes– Product information– Product popularity – Number of sellers

w Alexa.com/Allwhois.com– Web traffic, external links (consumer awareness)– Time of online market entry

w BBB, BizRate, Gomez, Truste, Verisign– Third party certification/recommendation (trust)


Advantages of Internet Data

w Price quoted at the same time

w Can focus on identical products

w Large number of categories can be studied

w Price levels from a couple of dollars to thousands of dollars


Data: Measures of Price Dispersion

w Range of price difference

w Percentage price difference: (max-min)/mean

w Standard deviation

w Variance

w Coefficient of variation: σ/ µ


Category N Obs Mean

Std Dev Min Max

BOOK 1155 20.96 24.10 2.75 212.00

CD 403 13.48 2.71 7.99 23.93

DESKTOP PC 976 1215.45 1079.86 208.60 5831.00

D V D 1241 25.00 15.98 4.99 149.98

LAPTOP PC 1073 2441.66 664.48 946.95 4632.99

PDA 474 424.17 281.66 16.42 1574.00

SOFTWARE 668 292.31 664.98 16.39 7752.00

ELECTRONIC 749 415.95 445.58 79.99 3999.99

Total 6739 678.84 1026.43 2.75 7752.00

Summary Statistics of Price by Category


Percentage of Price Differences by Category

Category Number

of Obs Mean Std Dev Minimum Maximum

Book 105 48.9% 13.8% 16.7% 94.2%

CD 43 51.0% 18.4% 20.3% 77.7%

DVD 96 43.7% 16.7% 16.9% 96.6%

Desktop 105 34.4% 27.1% 0.4% 119.7%

Laptop 78 25.7% 13.9% 1.4% 66.9%

PDA 37 37.1% 24.4% 8.2% 104.2%

Software 51 35.6% 25.9% 11.0% 106.0%

Electronics 66 31.0% 11.7% 10.3% 66.2%


Summary Statistics ofPrice Dispersion Measures

Variable N Mean Std Dev Min Max

P_RANGE 581 182.45 323.90 1.40 3452.05

P_DIFF 581 38.5% 20.97% 0.4% 119.75%

P_STD 581 55.04 95.29 0.43 941.20

P_VAR 581 12092.89 51511.67 0.19 885864.96

P_CV 581 11.72 6.21 0.15 36.49

P_LEVEL 581 651.99 995.01 3.56 5437.97


Operationalization: Independent Variables

w Product category dummies (category characteristics)

w Popularity of product (more information, more word of mouth)

w Average price level (involvement) (Moorthy, Ratchford, and Talukdar 1997)

w Number of competitors in market (Cohen 2000)


Analysis

w Factor analysis of e-tailer variables

w Cluster analysis of e-tailers on e-tailer service attributes

w Regression of price dispersion on category, e-tailer and market characteristics

w Hedonic regression of price on e-tailer characteristics


Factor Analysis Results:e-tailer Characteristics

Component Variable 1 2 3 4 5 Ease of Ordering .057 .775 .360 .255 .326 Product Selection .124 .757 .305 .180 .281 Product Information .121 .232 .948 .103 .133 Price .015 .263 .122 .145 .940 Web Site Navigation .123 .806 .189 .203 .380 On-Time Delivery .897 .074 .165 .233 .112 Product Representation .811 .140 .320 .252 .245 Customer Support .838 .128 .216 .386 .056 Tracking .868 .200 .173 .218 .031 Shipping & Handling .157 .172 .105 .950 .168

Factor Name Reliability Shopping

Convenience Product

Information

Shipping and

Handling Pricing

Extraction: Principal components. Rotation: Equimax with Kaiser normalization.


Clustering Analysis Results: e-tailer Clusters

Cluster Factor Score

1 2 3 Reliability .495 .192 -.262 Shopping Convenience

.819 -.771 .441

Product Information

-1.920 .151 .244

Shipping and Handling

.322 .524 -.474

Pricing .058 -.279 .225 Percent of Sample in Cluster

9.5% 40% 50.5%

Cluster is comprised of e-tailers who are:

Reliable and Convenient,

Uninformative, Medium Price

Inconvenient, Good Shipping,

High Price

Convenient, Informative,

Poor Shipping, Low Price


Cluster Analysis Results: e-tailer Clusters

w Cluster 1: Target consumers who know clearly what to buy, and focus on fulfilling customer orders (e.g., CDUniverse.com, CompUSA.com)

w Cluster2: Target price insensitive consumers with high search costs, attract and retain customers by superior shipping and handling service (e.g., Outpost.com, Powells.com)

w Cluster 3: Target price sensitive consumers with need for low price and superior information, but consumers regard these e -tailers as least reliable (e.g., Amazon.com, Barnes&Noble, Egghead.com, and eToys.com).

w e-tailers do not try to excel in every dimension. They target different consumer segments and position on different dimensions.


Price Dispersion Regression

w Dependent variable– Price dispersion in different measures

w Independent variables– Variation in e-tailer service attributes (the 5 factors)

– Variation in other e-tailer characteristics(time of online market entry, third party certification, traffic, external links)

– Market characteristics(number of sellers, consumer involvement, product popularity)

– Product characteristics (category dummies)


GMM Regression Results of Drivers of Price Dispersion Measure of variation in e-tailer characteristics

Range of e-tailer characteristics Standard Deviation of e-tailer

characteristics Measure of price dispersion (dependent variable)

Price Range Price STD Price Range Price STD

F Statistics P < 0.001 P < 0.001 P < 0.001 P < 0.001 Adjusted R2 92.79% 93.57% 92.60% 93.48% Intercept 0.677 0.355 -0.383 -0.780 Variation in online market entry -0.285 c -0.249 c -0.005 -0.049 Variation in consumer awareness -0.003 -0.016 0.005 -0.012 Variation in third party certification -0.189 c -0.189 c -0.066 -0.084 Variation in reliability 0.076 0.104 c 0.064 0.086 Variation in shopping convenience 0.646 a 0.514 a 0.539 b 0.455 b Variation in product information providing 0.594 a 0.520 a 0.628 a 0.550 a Variation in shipping and handling 0.256 a 0.196 b 0.292 a 0.215 b Variation in overall pricing 0.589 a 0.565 b 0.780 a 0.715 a Product popularity 0.040 0.036 0.055 0.051 Number of competitors -0.499 a -0.751 a 0.001 -0.025 a Consumer involvement (price level) 0.920 a 0.925 a 0.911 a 0.918 a CD * -0.408 a -0.496 a -0.259 c -0.346 a DVD -0.113 -0.183 -0.060 -0.104 Laptop 0.077 -0.068 0.285 0.149 Desktop 0.006 -0.127 0.186 0.079 Personal Digital Assistant -0.008 -0.163 0.168 0.029 Consumer Electronics -0.009 -0.092 0.145 0.078 Computer Software -0.066 -0.154 0.162 0.087 Dependent and non-dummy independent variables are measured in natural logs. a p< 0.01; b p < 0.05; c p < 0.1. * The base category is book.


Results: Price Dispersion Regression

w Price dispersion is largely explained by the drivers we investigated (Adjusted R2 > 92%)

w e-tailers’ characteristics & market characteristics lead to price dispersion

w Category uniqueness is not significant

w For high involvement/more expensive products, the absolute price dispersion is larger, but the relative price price dispersion (price dispersion deflated by mean price) is smaller

w Price dispersion decreases with larger number of competitors, but at a diminishing rate


Percentage of Variance Explained by Drivers of Price Dispersion

Measure of variation in e-tailer characteristics

Range of e-tailer characteristics

Standard Deviation of e-tailer characteristics

Measure of price dispersion (dependent variable)

Range Percentage Difference

Std CV Range Percentage Difference

Std CV

e-tailer Characteristics

3.6% 29.5% 2.7% 21.5% 3.1% 27.1% 2.3% 25.8%

Market Characteristics

88.8% 9.5% 90.1% 13.7% 88.9% 7.7% 90.8% 10.3%

Product Characteristics

0.4% 3.0% 0.6% 5.9% 0.6% 6.0% 0.4% 4.3%

Error

7.2% 58.0% 6.4% 58.9% 7.4% 59.2% 6.5% 59.7%


Hedonic Price RegressionS t a n d a r d i z e d C o e f f i c i e n t s o f H e d o n i c P r i c e R e g r e s s i o n o n e - T a i l e r C h a r a c t e r i s t i c s

C a t e g o r y B o o k C D D V D D e s k t

o p L a p t o

p P D A

S o f t wa r e

C o n s um e r

E l e c t r on i c s

M o d e l S i g n i f i c a n c e

P < 0 . 0 0 1

p < 0 . 0 0 1

P < 0 . 0 0 1

p < 0 . 0 0 1

p < 0 . 0 0 1

p < 0 . 0 0 1

p < 0 . 0 0 1

p < 0 . 0 0 1

A d j u s t e d R 2 3 9 % 5 8 % 2 0 % 4 1 % 5 % 3 4 % 3 9 % 1 0 %

T i m e o f O n l i n e M a r k e t E n t r y

0 . 0 5 - 0 . 0 2 - 0 . 1 3 a

- 0 . 4 8 a

- 0 . 0 6 c

- 0 . 1 3 a

- 0 . 1 9 a

- 0 . 1 1 c

C o n s u m e r A w a r e n e s s

0 . 1 0 a

0 . 0 8 a

0 . 0 2 - 0 . 2 7 a

- 0 . 0 2 - 0 . 1 9 a

- 0 . 2 9 a

- 0 . 0 5

T h i r d P a r t y C e r t i f i c a t i o n

0 . 0 5 0 . 0 9 - 0 . 0 5 c - 0 . 1 4 a 0 . 0 1 0 . 0 4 0 . 0 6 0 . 1 0 b

R e l i a b i l i t y - 0 . 1 0 a

0 . 0 7 c

0 . 0 3 0 . 2 3 a

- 0 . 2 0 a

0 . 2 5 a

0 . 2 7 a

- 0 . 1 5 a

S h o p p i n g C o n v e n i e n c e

0 . 7 3 a 0 . 1 6 b - 0 . 1 0 a 0 . 3 1 a - 0 . 0 1 0 . 1 2 a 0 . 2 9 a 0 . 1 0

P r o d u c t I n f o r m a t i o n

- 0 . 8 3 a

- 0 . 5 8 a

- 0 . 1 8 a

- 0 . 3 8 a

0 . 0 7 a

- 0 . 4 4 a

- 0 . 5 4 a

0 . 0 2

S h i p p i n g a n d H a n d l i n g

0 . 0 6 b

0 . 1 2 c

- 0 . 0 2 - 0 . 0 4 0 . 0 8 b

0 . 0 4 0 . 0 2 0 . 1 3 b

O v e r a l l P r i c i n g - 0 . 7 5 a - 0 . 5 1 a - 0 . 2 8 a - 0 . 0 4 0 . 0 0 - 0 . 2 5 a - 0 . 3 3 a - 0 . 1 8 a

a p < 0 . 0 1 ; b p < 0 . 0 5 ; c p < 0 . 1 .


Results: Hedonic Price Regressionw e-tailers with superior services may actually charge either higher or

lower prices

w Consumers generally are willing to pay for convenience, especially for low involvement items

w Good product information does not translate to higher prices--could be offered with low price

w Product information provision has the largest effect size among e-tailer characteristics

w Superior shipping and handling service commands higher price

w First mover advantage in online markets

w Higher consumer awareness corresponds to higher price for low involvement categories, but lower price for high involvement categories

w Conflicting findings: – consumer awareness

– trust or third party certification

– reliability


Conclusions & Managerial Implications

w e-tailer characteristics is not primarily responsible for the

observed price dispersion (explains less than 30% of the

variation of price dispersion)

w e-tailers may not need to fear pure price competition online

w Early online entrants may be able to enjoy a little price

premium

w Superior service may not necessarily command a price

premium

w e-tailers do not have to price match competitors. They can

differentiate in other ways.


Limitations & Future Research

w Market equilibrium

w Longitudinal data

w Hedonic regression could consider the competition among e-tailers and potential moderating effects of the market


Q & A

why are prices of the same item not the same at my.com...

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