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

1. Eastern Michigan University
Department of Marketing
An Exploratory Model of Social Media Exposure
and 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, 2012
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2. Eastern Michigan University
Department of Marketing
Presentation 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
Research
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3. Eastern Michigan University
Department of Marketing
Social Media Usage Increases by the Minute
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4. Eastern Michigan University
Department of Marketing
What are Social Media?
Social media includes web-based and mobile based Social Media Examples
technologies which are used to turn communication into
interactive dialogue between organizations, communities,
and individuals (Margold and Faulds 2009).
Kaplan and Haenlein (2010) define social media as "a
group of Internet-based applications that build on the
ideological and technological foundations of Web 2.0, and
that allow the creation and exchange of user-generated
content.” Social media is ubiquitously accessible, and
enabled by scalable communication technologies.
This form of media ‘‘describes a variety of new sources
of online information that are created, initiated, circulated
and used by consumers intent on educating each other
about products, brands, services, personalities, and
issues’’ (Blackshaw and Nazzaro 2004).
A common thread running through all definitions of social
media is a blending of technology and social interaction
for the co-creation of value.
[Source: Margold and Faulds 2009]
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5. Eastern Michigan University
Department of Marketing
Social Media as Marketing Tools
The use of social media as marketing tools has
received increasing levels of attention by both
practitioners 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).
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6. Eastern Michigan University
Department of Marketing
Social Media Effectiveness
The rationale behind the increased
spending and greater marketing role is the
belief by many companies using social
media 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).
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7. Eastern Michigan University
Department of Marketing
Social 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.
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8. Eastern Michigan University
Department of Marketing
Social 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).
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9. Eastern Michigan University
Department of Marketing
Social 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).
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10. Eastern Michigan University
Department of Marketing
Social 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.
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11. Eastern Michigan University
Department of Marketing
Emerging Social Media Exposure Theory
A useful theoretical perspective for explaining social media effects may be found in
interpersonal influence theory (McGuire 1968), which suggests that susceptibility to
interpersonal influence (SII) is a trait that is partially reflected by self-selected exposure
to information sources.
The SII is manifested by normative conformance to social group values, or by the
seeking of information, that serves a risk reduction function (Bearden et al. 1989). It is
the information seeking aspect of SII that may explain how exposure to social media
affects consumer purchase decisions.
In addition, the theory of reasoned action (Fishbein and Ajzen 1975) may also help
explain the online purchase behaviors of the social media followers of e-retailers by
combining the information seeking aspect of the SII theory with the future intention
shaping effects of past purchase related experiences and behaviors.
The conceptual framework that emerges suggests that orientations or predispositions
for social media exposure and usage patterns affect past (distal) purchase behaviors
which in-turn affect current (proximal) behavioral intentions and purchases .
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12. Eastern Michigan University
Department of Marketing
Emerging Theoretical Framework
Partial anecdotal support for this theoretical
approach 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”)
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13. Eastern Michigan University
Department of Marketing
Emerging Theoretical Framework
Some academic research corroborates the role of social media information
exposure 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.
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14. Eastern Michigan University
Department of Marketing
Research Questions and Hypotheses
Based on the conceptual framework and prior research described above, there are six
research questions (and associated hypotheses) addressed in this study. All of the
hypothetical paths are expected to be positive (i.e., higher levels of the predictor are
expected to result in significantly higher levels of the criterion along each path
specified 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]
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15. Eastern Michigan University
Department of Marketing
Figure 1: Summary of Reviewed Literature and Hypotheses
Model—Figure 1
“Distal Effects” “Proximal Effects”
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16. Eastern Michigan University
Department of Marketing
Methodology:
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).
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17. Eastern Michigan University
Department of Marketing
Methodology:
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).
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18. Eastern Michigan University
Department of Marketing
Figure 2: Measurement Summary
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19. Eastern Michigan University
Department of Marketing
Methodology
Figure 3: Sample Refinement
To evaluate the proposed
framework a two-step approach
was 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).
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20. Eastern Michigan University
Department of Marketing
Findings: Table 1
Profile of the Sample
A comparison of those respondents who
actively followed retailers on social media
against those that did not, revealed some
striking 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
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21. Eastern Michigan University
Department of Marketing
Figure 4: Model Path Coefficients and Variance Explained
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22. Eastern Michigan University
Department of Marketing
Findings:
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
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23. Eastern Michigan University
Department of Marketing
Findings:
Measurement Summary Statistics
Table 4
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24. Eastern Michigan University
Department of Marketing
Findings:
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
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25. Eastern Michigan University
Department of Marketing
Findings:
Hypothesis T-tests
All hypotheses are supported by the findings.
Table 6
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26. Eastern Michigan University
Department of Marketing
Discussion:
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).
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27. Eastern Michigan University
Department of Marketing
Implications and Future Research Needs
The 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).
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28. Eastern Michigan University
Department of Marketing
Thank You for Your Attention
Are There Any Questions?
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