Impact of Social Networking Services on
e-Retailer Performance:
An Empirical Analysis
David Xiaosong Peng
Gregory R. Heim
Joobin Choobineh
Mays Business School, Texas A&M University
Agenda
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Define social networking services (SNS)
Motivation
Literature review
Research hypotheses
Data and empirical methods
Findings
Social Networking Services
Social Networking Services
• “An online service, platform, or site that
focuses on building and reflecting on social
networks or social relations among people,
e.g., who share interests and/or activities.”
(Wikipedia, 2011)
Motivation for Study
• Huge growth of social networking services over past
few years
• Many businesses today use social networking
services to connect with consumers and enhance
their operations
– Corporate/Fan pages
– Coupon generation applications
– Advanced IT (mobile) tools
• Instance of outsourcing of marketing, customer
relationship, and service delivery to a separate thirdparty firm
Motivation for Study
• Business press chatter about potential impacts from
social networking services (good and bad)
• Real business benefits of deploying social networking
applications and services still remains unclear
– Potential benefits
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Enhanced consumer services
Increased marketing effectiveness
Collect useful consumer feedback
Increased website traffic
– Potential downsides
• Customers can share negative experiences with peer groups
• Public posting of service failures
• Harm to business revenues
Motivation for Study
• Little empirical research has examined impact of
integrating a firm to social networking services
– Most prior research focuses on customer participation,
process-level analysis of activities taking place within
blogs/wikis
– Internet retailers provide a great example of a segment
directly impacted by social networks, yet little research in this
area
• Research Questions:
– (1) How does the use of social networking services impact
e-retailer financial and operating performance?
– (2) Which e-retailer merchandise categories benefit more
from using social networking services? And why?
Research Hypotheses
Related Literature
• Online communities and social networks
– User motivation to participate/contribute (Wasko and Faraj
2005; Jones et al. 2004)
– Formation, stability, sustainability of online communities
(Ransbotham and Kane 2011)
• Business value of information technology (IT)
– Business value of inter-organization electronic linkages
(Bharadwaj et al. 2006)
• Service outsourcing
– Social networking services provide many-to-many
transactions (Hof 2005)
– These services deliver applications through outsourcing
oriented business application service models (Dornan 2007)
Research Hypotheses
• Social networking and e-retailer performance
– Social network theory/social capital theory
• Social ties of an individual are viewed as valuable social
capital
• As network ties increase, individual’s ability to leverage
resources of network members increases
– Social networking services enable users to build
new social ties and reconnect.
– Social ties are valuable to e-retailers who can
gauge social patterns and personal interests, and
in turn, serve customers effectively
Research Hypotheses
Weak/No Social Ties
=
Less Value
Online
Retailer
Social
Network
Nodes/Social Ties
=
Valuable
Research Hypotheses
• Hypothesis 1:
– E-retailer use of social networking services
is positively associated with e-retailer
performance.
Research Hypotheses
• Moderating effect of merchandise categories
– Prior literature suggests that consumer buying
characteristics vary across product categories
• Convenience goods vs. shopping goods vs. specialty
goods (Copeland 1924)
• Convenience vs. non-convenience goods (Porter 1976)
• Search goods vs. experience goods (Nelson 1970)
Research Hypotheses
• Online shopping exhibits similar patterns
– Consumers more likely to shop online for search goods than
experience goods (Bhatnagar et al. 2000, Girald et al. 2002)
– Order fulfillment customer satisfaction differs by product
category (Thirumalai and Sinha 2005)
• Due to classification difficulties, search/experience
can be replaced by metric representing the benefits
of information search (product price) (Laband 1991)
– As purchase price rises, risk of a bad purchase rises, and
benefit from pre-purchase efforts to get information increase
– Social networking advice is not rich enough to allay risks
– Thus, social networking advice should benefit less risky
purchases more than expensive purchases
Research Hypotheses
• Hypothesis 2:
– E-retailer use of social networking services
will have a smaller impact on e-retailer
performance in the more expensive
merchandise categories.
Data and Empirical Methods
Data
• Data source
– Internet Retailer Top 500 Guide annual survey
and ranking of the top internet retailers in the
United States (2008, 2009)
• Level of analysis
– Yearly data on e-retailer operations
• Number of observations
– Approximately 1000 observations in total (pooled)
• 967 balanced panel observations (firm exit from/entry
into survey)
• 409 first-differenced observations
Variables
• Dependent variable
– Web sales
– Monthly visitors
– Monthly unique visitors
• Key variables of interest
– Social networking use
– High average ticket
– Social networking use * High average ticket
• Control variables
– Rank in the merchandise category
– Share in the merchandise category
– Herfindahl index in the merchandise category
Variables
Variables
Social Networking Use = Index of Weighted Traffic across 4 Social Networking
Services in which the e-retailer participates
Variables
High Average Ticket = Dichotomous; Divides e-retailers up by High Value
Merchandise Category (=1) vs. Low Value Category (=0)
Variables
Variable Summary Statistics
Empirical Model
Fixed Effects Model
Estimated using XTREG, -fe + cluster robust SE’s
First Difference Model
Eliminates fixed effect; Estimated using OLS
Empirical Model
Taylor Hausman Model
Estimates time-invariant variables; Estimated using XTHTAYLOR
Estimation Method
• Estimated models using Stata 10.1
• Estimation methods
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Fixed Effects (FE) estimators
First Difference (FD) estimators
Hausman-Taylor (HT) estimators
Pooled regressions for individual social networking
services
Research Findings
Regression Results
Regression Results
Discussion of Findings
• Hypothesis 1:
– E-retailer use of social networking services is
positively associated with e-retailer performance.
– Supported for Web Sales, Monthly Visitors
– Not supported for Unique Monthly Visitors
(opposite of expected sign; weakly significant)
Discussion of findings
• Hypothesis 2:
– E-retailer use of social networking services will
have a smaller impact on e-retailer performance in
the more expensive merchandise categories.
– Strong support for Web Sales, Monthly Visitors
– Weak support for Unique Monthly Visitors
Regression Results:
Robustness Analysis
Analysis of Individual Social
Networking Services
Limitations
• Data obtained from an external source
– Cannot control data collection process
• Only two years of panel data
• Sample constrained to top internet retailers in USA
– Top 500 retailers cover large % of total e-retail business
– E-retailers in other nations may differ
– Results at lower-tier e-retailers may differ
• Data on social networking traffic constrained to four
social network services
– Top two cover over 75% of social network traffic
Limitations
• Potential omitted variables
– Pre-existing online marketing practices
– Offline advertising practices
– Utilization effectiveness of social networking service
processes and customer data
– Quality of service provided by social networking services
• Endogeneity concerns
– Use of social networking service is a managerial decision,
which may lead to over- or under-estimation of effect
• Causality concerns
– Data is observational; we did not perform a controlled
experiment
Future Research
• Presently updating study
– Include additional years of data
– Include omitted variables
– Potentially use instrumental variables to alleviate
endogeneity concerns
– Potentially use post-hoc analysis methods to make stronger
causal statements
• Many other research issues on social networking and
its effects on service operations
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Descriptive (typology) of how SNS are used
How to make best business use of SNS
How consumers operate within SNS
Financial benefits of SNS over longer periods