Although there has been a steady growth in multichannel retailing, few studies examine how different channels of information search affect customers’ purchase behavior. As the retailing industry evolves toward multichannel or omnichannel retailing, customers may use one channel to search for information and purchase in another channel. For example, customers can get product information in a brick-and-mortar retail store and then purchase a product online, referred to as “showrooming.” Alternatively, customers may go online to search product information but then go to a brick-and-mortar retail store to complete their purchase, referred to as “webrooming.” Besides, customers can evaluate product attributes by touch and feel the product in the store and they can simultaneously get additional information using an online search at the brick-and-mortar retail store, and then make a purchase decision in the brick-and-mortar retail store or in the online channel. In this study, we compare the effect of offline information sources (e.g., advertising/direct marketing, conversation with friend or family) and online information sources (e.g., online advertising, email marketing) on customers’ purchase behavior in both online and offline channels. In addition, we also examine the influence of in-store online information search on in-store and online purchase behavior. We test our conjectures by using data from more than 700 respondents of the 2014 National Technology Readiness Survey (NTRS), who have made personal purchase for a various types of products where the total amount of the transaction was at least $50 in the past 3 months. We find that offline information source is positively and strongly associated with the likelihood of purchasing in a brick-and-mortar retail store, but we see a significant negative association between use of an online information source and probability of purchase in a brick-and-mortar retail store. These results elucidate the importance of channel consistency between information search and purchase. Interestingly, we find counterintuitive evidence of showrooming and webrooming behavior: while in-store online search has significant and positive correlation with in-store purchase behavior, in-store online search decreases the probability of purchasing online. These results provide new insights for customer behavior in multi-channel settings and provide implications for designing marketing interventions.

1. 1 Dongwon LeeFrontiers 2016
Dongwon Lee
Robert H. Smith School of Business
University of Maryland
Showrooming vs. Webrooming: The Effect of Multichannel Information
Search on Purchase Behavior
25 June 2016, 1030-1055 am (This version 22 June 2016)
Frontiers 2016
Sunil Mithas
Robert H. Smith School of Business
University of Maryland
Gina Woodall
Rockbridge Associates, Inc

2. 2 Dongwon LeeFrontiers 2016
INTRODUCTION
Shift in Shopping
For the first time, online shoppers bought more of their purchases online
rather than in stores.

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SHOWROOMING VS. WEBROOMING
Showrooming vs. Webrooming

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RESEARCH QUESTION
Research Question 1:
How do offline/online information sources influence
online/brick-and-mortar store purchase?
Research Question 2:
How does in-store online information search influence
online/brick-and-mortar store purchase?
Information sources
online
offline
Purchase
online store
offline store
In-store online information search

5. 5 Dongwon LeeFrontiers 2016
ACADEMIC BACKGROUND
Multi-Channel Customer Management
Implementation of buy-online,pick up in store (BOPS) is associated with a reduction in
online sales and an increase in store sales and traffic (Gallino and Moreno 2014).
When a store opens locally, people substitute away from online purchasing (Forman et al.
2009).
The introduction of an offline channel increases demand overall and through the online
channel as well (Bell et al. 2013).
 E.g., Warby Parker and Amazon offline store
 Despite the growing importance of showrooming and webrooming in practice,
comparison between showrooming and webrooming has not been studied

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METHODS AND DATA
National Technology Readiness Survey 2014
Authored by Parasuraman and
Rockbridge, Co-sponsored by the
Center for Excellence in Service
TRI Scale licensed to over 120
scholars in 30 countries, including
Germany, Turkey, China, UK, Brazil,
India, Malaysia, Philippines,
Canada, South Africa
Nationally representative survey of
U.S. adults
Frame: online panel from 2
reputable providers
Weighted to match U.S. Census
Margin of Error: +/- 3%
Final sample in this analysis: 705
respondents
Source: Rockbridge Associates (2015)

7. 7 Dongwon LeeFrontiers 2016
METHODS AND DATA
Empirical Model
𝐿𝑜𝑔𝑖𝑡 (𝑂𝑓𝑓𝑙𝑖𝑛𝑒 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑖)
= 𝛽0 + 𝛽1 𝑂𝑓𝑓𝑙𝑖𝑛𝑒𝐼𝑛𝑓𝑜𝑖 + 𝛽2 𝑂𝑛𝑙𝑖𝑛𝑒𝐼𝑛𝑓𝑜𝑖 + 𝛽3 𝐼𝑛𝑆𝑡𝑜𝑟𝑒𝑂𝑛𝑙𝑖𝑛𝑒𝑆𝑒𝑎𝑟𝑐ℎ𝑖 + γ𝑃𝑖 + δ𝐶𝑖 + 𝜀𝑖
Dependent Variable
 Offline Purchase: dummy variable for purchase channel (1=offline, 0=online)
Independent Variables
 Offline Information: use any offline information sources (1=yes, 0=no)
 Online Information: use any online information sources (1=yes, 0=no)
 In-Store Online Search: use any online information sources at an offline store (1=yes, 0=no)
Product Controls (P)
 Product Price, Product Categories (16 dummy variables)
Customer Controls (C)
 Age, Gender, Marital Status, Living Area (1=city or suburb, 0=rural or small town), Technology Related
Job, Race, Born in US

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METHODS AND DATA
Descriptive Statistics and Correlations (n=705)
Mean SD 1 2 3 4 5 6 7 8 9 10 11
1 Offline Purchase 0.63 0.48 1.00
2 Offline Information 0.84 0.36 0.17* 1.00
3 Online Information 0.71 0.46 -0.36* 0.13* 1.00
4 In-store Online Search 0.14 0.34 0.05 0.14* 0.25* 1.00
5 Product Price (log) 5.21 1.11 0.02 0.12* 0.15* 0.13* 1.00
6 Age 41.61 12.47 0.09* -0.06 -0.22* -0.17* -0.03 1.00
7 Male 0.49 0.50 -0.03 -0.04 0.00 0.04 0.01 0.04 1.00
8 Marry 0.55 0.50 0.04 0.00 0.00 0.02 0.07* 0.15* 0.00 1.00
9 City 0.74 0.44 0.02 0.05 -0.02 0.00 -0.05 -0.06* -0.01 -0.06* 1.00
10 Tech Job 0.13 0.34 -0.06 -0.02 0.07 0.13* 0.05 -0.07* 0.19* 0.11* -0.04 1.00
11 White 0.69 0.46 0.02 -0.03 -0.05 -0.04 0.00 0.16* 0.07* 0.10* -0.15* 0.03 1.00
12 Born US 0.89 0.31 0.05 -0.01 -0.07* -0.01 0.02 0.03 0.00 0.03 -0.06 -0.05 0.28*
*significant at p<0.05

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RESULTS
(1) (2) (3) (4)
Dependent Variables Offline Purchase Offline Purchase Offline Purchase Offline Purchase
Offline Information 1.703*** 1.691*** 1.722*** 1.693***
(0.278) (0.286) (0.282) (0.288)
Online Information -2.766*** -2.726*** -2.825*** -2.804***
(0.296) (0.298) (0.305) (0.307)
In-Store Online Search 0.695*** 0.776*** 0.570** 0.619**
(0.253) (0.257) (0.263) (0.268)
ln(price) 0.094 0.082
(0.085) (0.086)
Age 0.009 0.006
(0.007) (0.008)
Male -0.250 -0.243
(0.177) (0.194)
Marry -0.022 -0.183
(0.180) (0.194)
City 0.031 0.059
(0.201) (0.214)
Tech Job -0.246 -0.234
(0.269) (0.277)
White -0.074 -0.119
(0.195) (0.204)
Born US 0.502* 0.479*
(0.278) (0.284)
Constant 1.181*** 0.548 0.410 0.110
(0.274) (0.512) (0.545) (0.675)
Product Category X X O O
Observations 705 705 705 705
Wald χ2 98.66*** 99.80*** 118.46*** 121.56***
Log pseudolikelihood -383.646 -379.167 -375.646 -372.226
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

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RESULTS
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
(1) City/Suburb (2) Rural/Small town (3) Total
Dependent Variables Offline Purchase Offline Purchase Offline Purchase
Offline Information 1.531*** 2.506*** 1.717***
(0.343) (0.599) (0.289)
Online Information -2.849*** -3.043*** -2.817***
(0.345) (0.641) (0.310)
In-Store Online Search 1.022*** -0.211 -0.228
(0.333) (0.587) (0.460)
In-Store Online Search 1.203**
x City (0.552)
City -0.152
(0.234)
Constant -0.692 2.787* 0.279
(0.797) (1.437) (0.683)
Product Controls O O O
Customer Controls O O O
Observations 522 183 705
Wald χ2 106.83*** 49.18*** 122.58***
Log pseudolikelihood -268.006 -88.691 -359.742
Location (City/Suburb vs. Rural/Small town) and
In-Store Online Search
In-store online search affects offline purchase only in city/suburb but not in
rural/small town areas. Is this result because of better availability of SKUs in
cities/suburbs than in rural/small town areas?

11. 11 Dongwon LeeFrontiers 2016
RESULTS
Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
(1) (2) (3) (4)
Dependent Variables Offline Purchase Offline Purchase Offline Purchase Offline Purchase
Offline Information 1.892*** 1.888*** 1.903*** 1.881***
(0.296) (0.304) (0.304) (0.310)
Online Information -2.855*** -2.817*** -2.898*** -2.880***
(0.316) (0.320) (0.324) (0.327)
In-Store Online Search 15.833*** 16.086*** 15.060*** 15.300***
(0.646) (0.732) (0.700) (0.790)
In-Store Online Search -15.258*** -15.434*** -14.612*** -14.808***
X Offline Information (0.685) (0.760) (0.737) (0.819)
Constant 1.096*** 0.435 0.341 0.032
(0.272) (0.511) (0.546) (0.676)
Product Controls X X O O
Customer Controls X O X O
Observations 705 705 705 705
Wald χ2 644.94*** 535.70*** 597.90*** 504.14***
Log pseudolikelihood -379.635 --375.316 -372.033 -368.258
Interaction Effect of Offline Information Source and
In-Store Online Search
Results indicate that in-store online search acts as a substitute of offline
information.

12. 12 Dongwon LeeFrontiers 2016
Information
Search
Purchase channel
OnlineOffline
Offline
Online
Traditional Brick-and-Mortar
Increase in probability of in-
store purchase
Showrooming
Decrease in
probability of online
purchase
Webrooming
Decrease in probability of in-
store purchase
Instore online search
increases offline purchases,
more effective in city/suburb,
negatively moderates the effect
of offline sources
Traditional
ecommerce
Increase in
probability of
online purchase
SUMMARY OF MAIN RESULTS

13. 13 Dongwon LeeFrontiers 2016
• Among offline sources, employees, packaging
information and store display play an important positive
role in offline purchase
• Among online sources, retailers’ website, app,
consumer reviews, and online employees play an
important negative role in offline purchase
• The positive role of offline employees dominates that of
online employees
• Robustness check: Models with continuous count
measures of online and offline sources yield broadly
similar results
ADDITIONAL FINDINGS

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ADDITIONAL FINDINGS
.2.4.6.8
1
0 1 2 3 4 5 6 7
# of Offline Sources
w/o In-store Online Search w/ In-store Online Search

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IMPLICATIONS FOR RESEARCH
Extends the growing literature on multi-channel retailing by documenting
new findings for information search and purchase behavior across channels
Showrooming vs. Webrooming: Offline information search decreases online purchase
probability; Online information search decreases offline purchase probability
In-store online information search
 In-store online information search increases offline purchase probability
 In-store online information search can be a substitute of offline information search for
offline purchase
 Positive effect of in-store information search on offline purchase probability applies only
for customers in city/suburb rather than in rural/small town area

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MANAGERIAL IMPLICATIONS
Strategies for managing information sources and information search for omni-
channel management for brick-and-mortar and online retailers
 Importance of channel consistency between information search and purchase
 Developing consistent and optimal customer experience across channels
 Move toward becoming dual-channel retailers
For brick-and-mortar retailers
 Integrate in-store and online channels
 Focus on providing information and services consistently
For online retailers
 Provide competitive prices and neatly curated contents
 Enable customers to use physical channel as showroom and pickup points

17. 17 Dongwon LeeFrontiers 2016
THANK YOU