Showrooming vs. Webrooming: The Effect of Multichannel Information Search on Purchase Behavior

1 Dongwon LeeFrontiers 2016

Dongwon LeeRobert 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 MithasRobert H. Smith School of Business

University of Maryland

Gina WoodallRockbridge Associates, Inc


INTRODUCTION Shift in Shopping

For the first time, online shoppers bought more of their purchases online rather than in stores.


SHOWROOMING VS. WEBROOMING Showrooming vs. Webrooming


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

RQ1

In-store online information searchRQ2


ACADEMIC BACKGROUNDMulti-Channel Customer ManagementImplementation 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, com-

parison between showrooming and webrooming has not been studied


METHODS AND DATANational Technology Readiness Survey 2014

Authored by Parasuraman and Rockbridge, Co-sponsored by the Center for Excellence in ServiceTRI Scale licensed to over 120 scholars in 30 countries, including Germany, Turkey, China, UK, Brazil, India, Malaysia, Philippines, Canada, South AfricaNationally representative survey of U.S. adultsFrame: online panel from 2 reputable providersWeighted to match U.S. CensusMargin of Error: +/- 3%Final sample in this analysis: 705 respondents

Source: Rockbridge Associates (2015)


METHODS AND DATAEmpirical Model

𝐿𝑜𝑔𝑖𝑡 (𝑂𝑓𝑓𝑙𝑖𝑛𝑒 h𝑃𝑢𝑟𝑐 𝑎𝑠𝑒¿¿ 𝑖)=𝛽0+𝛽1𝑂𝑓𝑓𝑙𝑖𝑛𝑒𝐼𝑛𝑓𝑜𝑖+𝛽2𝑂𝑛𝑙𝑖𝑛𝑒𝐼𝑛𝑓𝑜𝑖+𝛽3 h𝐼𝑛𝑆𝑡𝑜𝑟𝑒𝑂𝑛𝑙𝑖𝑛𝑒𝑆𝑒𝑎𝑟𝑐 𝑖+γ 𝑃 𝑖+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 Re-

lated Job, Race, Born in US


METHODS AND DATADescriptive 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


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 OObservations 705 705 705 705Wald 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


RESULTS


(1) City/Suburb (2) Rural/Small town (3) TotalDependent 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 OCustomer Controls O O OObservations 522 183 705Wald 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?


RESULTS


(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 OCustomer Controls X O X OObservations 705 705 705 705Wald 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.


Information Search

Purchase channel OnlineOffline

Offline

Online

Traditional Brick-and-MortarIncrease in probability of in-store purchase

ShowroomingDecrease in probability of online purchase

WebroomingDecrease in probability of in-store purchaseInstore 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


• 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


ADDITIONAL FINDINGS

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0 1 2 3 4 5 6 7# of Offline Sources

w/o In-store Online Search w/ In-store Online Search


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


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


THANK YOU

Showrooming vs. Webrooming: The Effect of Multichannel Information Search on Purchase Behavior

Retail

Transcript of Showrooming vs. Webrooming: The Effect of Multichannel Information Search on Purchase Behavior