A Exploratory Model of Social Media Exposure and Consumer Purchase Behavior on e-Retailer Websites


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A presentation given in Stockholm Sweden, June 28-30, 2012 at the International Conference on Research in Advertising (ICORIA).

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A Exploratory Model of Social Media Exposure and Consumer Purchase Behavior on e-Retailer Websites

  1. 1. Eastern Michigan University Department of MarketingAn Exploratory Model of Social Media Exposureand Consumer Purchase Behavior on e-Retailer Websites G. Russell Merz, Ph.D., Professor Department of Marketing Eastern Michigan University Presented at the 11th International Conference on Research in Advertising (ICORIA), Stockholm, Sweden, June 28-30, 2012EMU
  2. 2. Eastern Michigan University Department of MarketingPresentation Agenda• Introduction and Literature Review – Social Media Definitions – Social Media as a Marketing Tool – Social Media Effectiveness• Theoretical Framework – Social Media Exposure Model – Research Questions – Hypotheses• Methodology/Findings – Data Collection and Measurement – Analysis Methods – Sample Profile – Structural Equations Modeling (SEM) Results• Discussion – Contributions and Implications – Limitations and Directions for Future ResearchEMU 2
  3. 3. Eastern Michigan University Department of MarketingSocial Media Usage Increases by the MinuteEMU 3
  4. 4. Eastern Michigan University Department of MarketingWhat are Social Media? Social media includes web-based and mobile based Social Media Examplestechnologies which are used to turn communication intointeractive dialogue between organizations, communities,and individuals (Margold and Faulds 2009). Kaplan and Haenlein (2010) define social media as "agroup of Internet-based applications that build on theideological and technological foundations of Web 2.0, andthat allow the creation and exchange of user-generatedcontent.” Social media is ubiquitously accessible, andenabled by scalable communication technologies. This form of media ‘‘describes a variety of new sourcesof online information that are created, initiated, circulatedand used by consumers intent on educating each otherabout products, brands, services, personalities, andissues’’ (Blackshaw and Nazzaro 2004). A common thread running through all definitions of socialmedia is a blending of technology and social interactionfor the co-creation of value. [Source: Margold and Faulds 2009] EMU 4
  5. 5. Eastern Michigan University Department of Marketing Social Media as Marketing ToolsThe use of social media as marketing tools hasreceived increasing levels of attention by bothpractitioners and academic researchers. • In 2009, social media marketing spending was forecasted by eMarketer (2009, March 19) to grow at an average annual rate of 15% from $2.0 Billion in 2008 to $3.5 Billion in 2013. It was also reported that 63% of global companies surveyed by the Aberdeen Group planned to increase their social media marketing budgets in 2009 (eMarketer, 2009, March 23). • Recent reports indicate that only 26% of U.S. Marketers believe they can • Recent budget surveys suggest that these trends are effectively measure the ROI of social continuing and even increasing (Loechner 2011) media marketing investments despite difficulties in assessing the spending ROI. (eMarketer 2011, December 16). • Expanding interest in the marketing value of social • In addition, while 52% of marketers media has increased the search for social media report that their brands enjoy greater metrics that put a monetary value on a “fan” or a “like” influence because of social media (Goetzl 2009). presence, only 17% have integrated social media into the overall marketing mix (Irwin 2011). EMU 5
  6. 6. Eastern Michigan University Department of MarketingSocial Media EffectivenessThe rationale behind the increasedspending and greater marketing role is thebelief by many companies using socialmedia that they are effective. • By influencing brand reputation, increasing awareness, improving search rankings and site traffic (eMarketer 2009, July 29) and new customer acquisition (eMarketer 2012, January 30). • A study conducted by Starcom MediaVest Group (SMG) found that consumers taking actions on a brand’s social media page were 78% more likely to consider or make a purchase from that brand in the future (Kite 2011, September 6). EMU 6
  7. 7. Eastern Michigan University Department of MarketingSocial Media Effectiveness• Kamal and Carl (2011) report that exposure to social media in combination with one or more other channels, was linked to changes in brand perceptions, and increases in spending and consumption.• A BzzAgent study reported by eMarketer (2012, January 4) found that integrated social media campaigns boosted brand recommendation likelihood and purchase intentions by 20-30%, and the lift persisted for up to a year. EMU 7
  8. 8. Eastern Michigan University Department of MarketingSocial Media Effectiveness• These types of reported brand performance outcomes coupled with the fear of missing out (FOMO), may be leading marketers to increase their presence across multiple platforms beyond Facebook, Twitter and LinkedIn (eMarketer 2012, January 23). EMU 8
  9. 9. Eastern Michigan University Department of MarketingSocial Media Effectiveness• In addition, a recent study by ClearSaleing reported that consumers exposed to social media in addition to other online ad formats or marketing channels had average revenue per order of $280 (eMarketer 2012, February 16). EMU 9
  10. 10. Eastern Michigan University Department of MarketingSocial Media Effectiveness• However, less positive observations have also been made, for example there is some evidence that consumers do not talk about brands on social sites (eMarketer 2012, January 10), and a Forrester/SGI study recently reported that social media have almost no influence on online purchasing behavior (Wasserman 2011). These conflicting conclusions across studies reflect the lack of a theoretical foundation for explaining how social media effects and benefits are realized and potentially measured. EMU 10
  11. 11. Eastern Michigan University Department of MarketingEmerging Social Media Exposure TheoryA useful theoretical perspective for explaining social media effects may be found ininterpersonal influence theory (McGuire 1968), which suggests that susceptibility tointerpersonal influence (SII) is a trait that is partially reflected by self-selected exposureto information sources.The SII is manifested by normative conformance to social group values, or by theseeking of information, that serves a risk reduction function (Bearden et al. 1989). It isthe information seeking aspect of SII that may explain how exposure to social mediaaffects consumer purchase decisions.In addition, the theory of reasoned action (Fishbein and Ajzen 1975) may also helpexplain the online purchase behaviors of the social media followers of e-retailers bycombining the information seeking aspect of the SII theory with the future intentionshaping effects of past purchase related experiences and behaviors.The conceptual framework that emerges suggests that orientations or predispositionsfor social media exposure and usage patterns affect past (distal) purchase behaviorswhich in-turn affect current (proximal) behavioral intentions and purchases . EMU 11
  12. 12. Eastern Michigan University Department of MarketingEmerging Theoretical FrameworkPartial anecdotal support for this theoreticalapproach appears in some practitioner reports. • For example, consumers were reported to be increasing their reliance on social media to access information and share customer care experiences with companies and brands (eMarketer 2008, October 2). As a result, online retailers were reportedly maintaining greater presence on social networking sites. • Social networks and blogs were increasingly seen as important sources of purchase related information by consumers (eMarketer 2009, March 17) with marketers concluding that user reviews, relationships with bloggers, and discussion groups and brand communities seen as the “best practice” social tactics (eMarketer 2009, July 29). • More recently Owyang (2012) has postulated the “Dynamic Customer Journey” for conceptualizing how customer actions result from interactions of information sources, new media forms and various delivery devices (i.e. “screens”) EMU 12
  13. 13. Eastern Michigan University Department of MarketingEmerging Theoretical FrameworkSome academic research corroborates the role of social media informationexposure on communications effectiveness and in purchase decisions. • Lee et al. (2006) found in an experimental setting that informational social influences moderated the relationships between perceptions of website performance and intentions to adopt an e-commerce website for shopping and purchase decisions. Higher levels of positive social feedback reinforced the relationships, increasing the potential for using the e-commerce websites. • Kim and Srivastava (2007) found evidence of the impact from social review and recommendation systems on decisions to visit or purchase from e-commerce websites. • Liang et al. (2012) found that the social support gained by members from a social media website along with the website quality influenced intentions to engage in social commerce, which they defined as the use of social media resources (friends, news feeds, etc.) for input and aid in shopping and purchase decisions. • Qin (2011) in a research study examining the association between “word-of-blog” volume and revenues for the movie industry found a time series-based bidirectional (Granger causal) predictive relationship using highly aggregated econometric type data. • Colliander and Dahlen (2011) also demonstrated that blogs provided higher publicity effectiveness than online magazines. • Dhar and Chang (2009) studied how the volume of user generated content on blogs and music review websites was related to the sales of 108 music albums sold on Amazon. They found that future sales were positively correlated with the volume of blog posts about an album, the record label (brand) and reviews from mainstream media sources. EMU 13
  14. 14. Eastern Michigan University Department of Marketing Research Questions and HypothesesBased on the conceptual framework and prior research described above, there are sixresearch questions (and associated hypotheses) addressed in this study. All of thehypothetical paths are expected to be positive (i.e., higher levels of the predictor areexpected to result in significantly higher levels of the criterion along each pathspecified in the model).• RQ1. Does the general use of information resources when shopping online affect the (a) use of social media to follow retailers, and (b) the level of recent purchase-related activity with the retailer? [H1a, b]• RQ2. Does use of social media to follow retailers affect (a) recent purchase-related activities, (b) social media purchase offer processing, and (c) social media purchase offer use? [H2a, b, c]• RQ3. Do levels of social media purchase offer processing affect recent social media purchase offer use? [H3]• RQ4. Do recent purchase-related activities affect (a) current website behavioral intentions and (b) current website purchases? [H4a, b]• RQ5. Does use of social media based offers from retailers affect (a) recent purchase-related activities, (b) current website behavioral intentions, and (c) current website purchases? [H5a, b, c]• RQ6. Do current website behavioral intentions goals affect current website purchases? [H6] EMU 14
  15. 15. Eastern Michigan University Department of MarketingFigure 1: Summary of Reviewed Literature and Hypotheses Model—Figure 1 “Distal Effects” “Proximal Effects”EMU 15
  16. 16. Eastern Michigan University Department of MarketingMethodology:Sample, Data Collection and Measurement• The sample used in this study came from a large-scale research project conducted by ForeSee, a commercial marketing research firm, in spring 2011. The data was collected from FGI Research’s SmartPanel™, a sample frame of 1.6 million US consumer households that have agreed to participate in opt-in surveys (FGI 2012).• Using a randomly distributed e-mail survey invitation, data was collected from more than 24,000 respondents who had visited one of the top 100 online retail sites reported in the 2011 Internet Retailer Top 500 Guide (Internet Retailer 2011) within a two week period prior to the date of the survey invitation (Freed 2011). EMU 16
  17. 17. Eastern Michigan University Department of MarketingMethodology:Sample, Data Collection and Measurement • The survey used contained scaled measures for rating website experiences, satisfaction, and future intentions. • Measures included self-reports about information resources used by the respondents for acquiring shopping information, retailer related social media usage, prior shopping behaviors and purchases on the retailer website, and the most recent shopping/ purchase experiences on the website. • Most of the items used for capturing past and recent purchase-related behaviors were nominal or multiple response type questions • The choice of using e-retailers as a focus for this study is supported by evidence that retail shoppers are heavier users of social media: o Kimberley (2010) reported on a research study that placed fashion retailers at the top of an index measuring brand success on social media. o Increases in online retail sales reportedly far outdistance those for bricks and mortar stores, increasing the likelihood that customers are exposed to social media messages about e-retailers (Mattioli 2011). o Attention on how social media affects the retail shopping experience both on-line and off- line (Boccaccio 2011, Evans 2011, eMarketer 2008, Kimberley 2010, Mattioli 2011, Neisser 2012, and Internet Retailer 2011, 2012). EMU 17
  18. 18. Eastern Michigan University Department of MarketingFigure 2: Measurement SummaryEMU 18
  19. 19. Eastern Michigan University Department of Marketing Methodology Figure 3: Sample RefinementTo evaluate the proposedframework a two-step approachwas used: • First, the social media usage of all survey respondents was examined. Of the 24,715 complete and usable surveys, 13,950 were identified as social media users. Of these, 3717 reported that they actively followed the most recently visited online retailer. These 3717 cases were used in the model- building phase of the analysis. • Second, a PLS path model was used to test the model structure and evaluate the hypothetical paths specified in the conceptual model. The analysis used SmartPLS™ software that provides a full range of capabilities as well as many quality evaluation tools for assessing analysis results (Ringle et al. 2005, Hair et al. 2011). EMU 19
  20. 20. Eastern Michigan University Department of Marketing Findings: Table 1 Profile of the SampleA comparison of those respondents whoactively followed retailers on social mediaagainst those that did not, revealed somestriking differences.• In comparisons across three groups of retail website visitors, active followers of retailers on social media (Group 3) show differences in their demographic profiles, and in their use of information resources. Table 2 EMU 20
  21. 21. Eastern Michigan University Department of MarketingFigure 4: Model Path Coefficients and Variance Explained EMU 21
  22. 22. Eastern Michigan University Department of MarketingFindings:Cross Correlations of Latent and Manifest Variables • The measurement model in PLS is assessed in terms of item loadings and reliability coefficients (composite reliability), as well as the convergent and discriminant validity. • Measures with loadings onto underlying latent variables of greater than 0.7 possess acceptable levels of association with a component (Fornell and Larcker 1981). Table 3 EMU 22
  23. 23. Eastern Michigan University Department of MarketingFindings:Measurement Summary Statistics Table 4 EMU 23
  24. 24. Eastern Michigan University Department of MarketingFindings:Indicators of Model Quality • Interpreted like a Cronbach’s alpha for internal consistency reliability, a composite reliability of 0.7 or greater is considered as an acceptable level of reliability (Fornell and Larcker 1981). • The average variance extracted (AVE) measures the variance captured by the indicators relative to the measurement error, and it should be greater than 0.5 to justify using a construct (Barclay, Thompson and Higgins 1995). Table 5 EMU 24
  25. 25. Eastern Michigan University Department of MarketingFindings:Hypothesis T-tests All hypotheses are supported by the findings. Table 6 EMU 25
  26. 26. Eastern Michigan University Department of MarketingDiscussion:Contributions The results of this exploratory study provide support for the general hypothesis that exposure to social media by customers of e-retailing websites has a positive effect on purchase related intentions and behaviors. • Consistent with the information seeking component of interpersonal influence theory (McGuire 1968, Bearden et al 1989) the findings reveal that customers with more diverse patterns of accumulated information exposure, of which social media is a large part, exhibit higher purchase rates. • Incorporating information seeking into a framework related to the theory of reasoned action (Fishbein and Ajzen 1975), reveals the direct effect of information seeking propensity onto past purchase related activities (such a as recent purchases online and off-line; and the use of social media based promotional offers), and the indirect effect on current website purchases. • The analysis results show that there is little immediate or direct effect attributable to social media exposure, but suggest its effect may accumulate over time reaching a threshold that is a tipping point. • The accumulated exposure, mediated by recent favorable purchase activities, explains a significant amount of the variance in the current purchase behaviors of e-retailing customer in this study. The mediation of recent purchase related activities between social media exposure and current visit purchases, is a finding unreported elsewhere. • Some possible explanations for how exposure accumulation occurs can be found in the recent work on brand social power and brand communities (Carlson et al. 2008) (Corsno et al. 2009), and in the importance of virtual communities as reference groups (Misra et al. 2008, Pentina et al. 2008). EMU 26
  27. 27. Eastern Michigan University Department of MarketingImplications and Future Research NeedsThe main implications derived from the results are:• The presence of retailers on social media appears to increase the likelihood of promotional message processing (H2c), promotional offer use (H2c), and website and store visits (H2a).• In addition, the greater the previous use of social media promotional offers, the greater the likelihood of current visit purchases (H5c).• Implementation of these findings requires thoughtful consideration of the role of social media within the retailer’s IMC program (Margold and Faulds (2009).Future research needs are:• First, the interpersonal influence theory suggests that information is sought to reduce risk, future studies should examine this aspect of information seeking through social media. The risk reduction benefit may be related to the diverse number of information resources used by those most likely to purchase from a retailer.• Second, the importance of the social media exposure most likely varies across business sectors. This study focused on the e-retailing sector, but exposure may be negligible and play little role in some sectors, or may have enhanced multiplier effects in others.• Third, many other outstanding questions remain, such as, the measurement of social ROI and secondary WOM effects (Hoffman and Fodor 2010, Trusov et al. 2009, Chu and Kim 2011), and privacy and invasiveness issues (Walsh 2012).• Finally, although some guidelines exist (Paynter 2010), little is known about how social media can be integrated into marketing plans to achieve objectives (Nelson-Field and Klose 2010). EMU 27
  28. 28. Eastern Michigan University Department of Marketing Thank You for Your Attention Are There Any Questions?EMU 28