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

1. 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

2. Agenda • Define social networking services (SNS) • Motivation • Literature review • Research hypotheses • Data and empirical methods • Findings

3. Social Networking Services

4. 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)

5. 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

6. 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

7. 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?

8. Research Hypotheses

9. 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)

10. 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

11. Research Hypotheses Weak/No Social Ties = Less Value Online Retailer Social Network Nodes/Social Ties = Valuable

12. Research Hypotheses • Hypothesis 1: • E-retailer use of social networking services is positively associated with e-retailer performance.

13. 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)

14. 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

15. 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.

16. Data and Empirical Methods

17. 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

18. 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

19. Variables

20. Variables Social Networking Use = Index of Weighted Traffic across 4 Social Networking Services in which the e-retailer participates

21. Variables High Average Ticket = Dichotomous; Divides e-retailers up by High Value Merchandise Category (=1) vs. Low Value Category (=0)

22. Variables

23. Variable Summary Statistics

24. Empirical Model Fixed Effects Model Estimated using XTREG, -fe + cluster robust SE’s First Difference Model Eliminates fixed effect; Estimated using OLS

25. Empirical Model Taylor Hausman Model Estimates time-invariant variables; Estimated using XTHTAYLOR

26. 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

27. Research Findings

28. Regression Results

29. Regression Results

30. 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)

31. 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

32. Regression Results:Robustness Analysis

33. Analysis of Individual Social Networking Services

34. 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

35. 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

36. 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