Impact of Social Networking Services on e-Retailer Performance:An Empirical Analysis

David Xiaosong PengGregory R. Heim

Joobin Choobineh

Mays Business School, Texas A&M University

Agenda

• 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 third-party 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

• 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

Social Network

OnlineRetailer

Nodes/Social Ties=

Valuable

Weak/No Social Ties=

Less Value

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

VariablesHigh Average Ticket = Dichotomous; Divides e-retailers up by High Value

Merchandise Category (=1) vs. Low Value Category (=0)

Variables

Variable Summary Statistics

Empirical ModelFixed Effects Model

First Difference Model

Estimated using XTREG, -fe + cluster robust SE’s

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– 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– 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