“The Role of Peer-to-Peer Communication in Online Sponsorship” Online communities are widely studied in marketing and information systems literature. However, research on the sponsorship effects in online communities is scarce. This research fills part of this gap by providing an analysis of the role of community commitment and social network communication in predicting the intention to purchase online sponsoring products. In particular, this study provides evidence of the relationship between peer-to-peer communication and online sponsorship effectiveness, examining the relationship between intention to purchase, online sponsorship outcomes (goodwill, attitude, and fit), commitment and activation in peer-to-peer communication. Through the analysis of a group of web communities (700) in an online sponsorship context, the authors demonstrate that people active in peer-to-peer communication show a greater intention to purchase online sponsoring products than passive community members. However, not all active members of a community are 1
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“The Role of Peer-to-Peer Communication in Online Sponsorship”
Online communities are widely studied in marketing and information systems literature.
However, research on the sponsorship effects in online communities is scarce. This research fills
part of this gap by providing an analysis of the role of community commitment and social
network communication in predicting the intention to purchase online sponsoring products. In
particular, this study provides evidence of the relationship between peer-to-peer communication
and online sponsorship effectiveness, examining the relationship between intention to purchase,
online sponsorship outcomes (goodwill, attitude, and fit), commitment and activation in peer-to-
peer communication. Through the analysis of a group of web communities (700) in an online
sponsorship context, the authors demonstrate that people active in peer-to-peer communication
show a greater intention to purchase online sponsoring products than passive community
members. However, not all active members of a community are similar: participants who share
information through different social networks are more sensitive to online sponsorship than
members who use emails.
Keywords: online sponsorship, peer-to-peer communication, online community, intention to
purchase, commitment.
Article classification: Research paper.
1
1 Introduction
Among the marketing advertising activities used by firms, sponsorship is one of the most studied
in the literature (Cornwell and Maignan, 1998; Rifon et al., 2004; Cornwell and Coote, 2005;
Simmons and Becker-Olsen, 2006). In general, sponsorship is an investment in an activity, cause,
or event (or a Web community) made in return for access to exploitable commercial potential
(Meenaghan, 1998).
Cause-related sponsorship, in particular, is a donation that makes an event or an organisation
possible. In doing so, firms hope to gain consumer attitude, goodwill and purchases (Cornwell
and Coote, 2005). The principles of cause-related sponsorship are based on cause-related
marketing, a strategy whereby firms make financial contributions and/or support non-profit
organisations in order to engage in a revenue-providing exchange that satisfies both business and
individual objectives (Varadarajan and Menon, 1988).
Many non-professional sport teams in Europe survive because of firms that support these groups
in an instrumental (e.g., t-shirts, games instruments) and/or expressive way (e.g., banners, logo
expositions and advertising). Thus, , sponsoring companies gain the appearance of “good
citizenship” (Rifon et al., 2004); and, they acquire short- and long-term influence on awareness
and identification of sponsors (Gwinner, 1997; Pham and Johar, 2001), attitude toward sponsors
After the test phase, the definitive version of the questionnaire was published, and links to the
questionnaire were inserted both on the web portal’s home page and on each team’s website.
5 Results
The online questionnaire was available to visitors to the portal and/or websites between June and
September 2009. During this period, 139 questionnaires were completed. After a preliminary
analysis to check the collected questionnaires’ quality, 130 of them were retained and analysed.
Observing the distribution of the respondents (n = 130) by their role on the team, we noticed that
managers represent the most significant category (43.8%), followed by players (30.8%), coaches
(10%), players’ relatives (7.7%) and fans (6.9%). Unfortunately, a comparison between the
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distribution of the self-selected sample and that of the target population is not possible; in fact,
only approximately 5% of registered members has indicated their current role in the community
when registering on the web portal. Because we are working with a self-selected sample, we
encounter the risk of collecting biased data; in this case, individuals in some categories could be
more likely to participate in the survey. However, the distribution of respondents by role
indicates that we obtained a well-balanced presence from all of the considered categories. This
finding also dovetails with the sponsors’ primary objective, to reach (and to get a complete
picture of) the community in its entirety.
An evaluation of the respondents’ level of involvement in team activities shows that 91.1% of
them regularly attend their team’s competitions, whereas 11.5% attend competitions with some
frequency, and 2.1% seldom attend. Moreover, 54.6% of the survey participants visit the team’s
website every day, with 26.2% visiting once a week and 17.7% only once a month. Thus, we can
conclude that the sample is generally composed of people who are highly involved in the
community and in both the online and the offline activities of the team.
All of the questionnaire’s variables have been tested for normality, and the exploratory factor
analysis was implemented to select the items most relevant to the intention to purchase
sponsoring products online: “attitude toward the online sponsor”, “goodwill toward the online
sponsor”, “sponsor congruence”, and “commitment to the team”. By applying factor analysis to
the 12 proposed items (using the maximum likelihood extraction and the orthogonal Varimax
rotation) and interpreting the factor loadings, we obtained the four expected independent
variables. Some items from the original list were deleted because they either do not load at the
0.3 level (suggested by Nunnally and Bernstein, 1994) or cross-load on several factors. Criteria
such as the eigenvalues (acceptable if ≥ 1), the level of factor loadings (≥ .45), the KMO (Kaiser-
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Meyer-Olkin) measure of sampling adequacy and the Bartlett’s test of sphericity were used to
derive the four components. These account for 75.22% of the total variance.
The main results of the factor analyses on the cited independent variables are summarised in
Table 1.
Table 1. Composition of measures and items descriptive statisticsFactors and items
(α = Conbrach’s alpha; EV = Eigenvalue; VAR = % of explained variance)
Std. Loading
Mean(min: -2;max: + 2)
S.D.
Goodwill toward the online sponsors( = 0.845; EV = 4.38; VAR = 39.87%)Online sponsors support our sport activities 0.84 .146 1.550Our team benefits from the online sponsors 0.75 .238 1.493Online sponsors are involved with their community 0.66 .315 1.525Attitude toward the online sponsors( = 0.783; EV = 1.59; VAR = 14.50%)Companies that sponsor my team online are professional 0.80 1.030 1.193Companies that sponsor my team online provide quality products/services 0.78 1.207 .764
Companies that sponsor my team online are leader in their industry 0.60 .476 1.376
Sponsor’s congruence( = 0.763; EV = 1.253; VAR = 11.40%)Online sponsors and our team have the same values 0.78 .169 1.463There is a logical connection between our team and the online sponsors 0.63 -.0615 1.503
The image of the online sponsors and that of my team are similar 0.54 -.453 1.409Commitment to the team( = 0.73; EV = 1.040; VAR = 9.45%)My friends view me as a strong fan of our team 0.84 .853 1.300I see myself as a strong fan of our team 0.71 1.284 1.087Intention to purchase online sponsoring products( = 0.88; EV = 2.92; VAR = 72.88%)I would definitely buy products/services from our online sponsors 0.85 .153 1.433
It is likely that I will buy the products/services of our team’s online sponsors 0.83 .430 1.441
I would try one of the products/services of our team’s online sponsors if they were available before or after a game 0.77 .538 1.370
My overall attitude toward purchasing products/services from our team’s online sponsors is positive 0.74 .853 1.335
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Moreover, a single factor analysis was conducted on the dependent variable to obtain a single
factor score used to estimate the proposed model, in terms of intention or likelihood to purchase
the sponsoring online brands or products.
Finally, we inserted in our model two more independent dummy variables: the independent
components “peer-to-peer communication by email” and “peer-to-peer communication in social
networks”. The first variable identifies people who use email to share information, whereas the
second one identifies people who use posts on social networks to share the information from the
sponsored web page.
To test our hypothesis, we estimated a regression model in order to assess the determinants for
the intention to purchase online sponsoring brands and products. In our analysis, we considered,
in particular, the attitude and the goodwill towards the online sponsors, the congruence between
online sponsors and the community (or the team), the commitment to the community and the
viral communication. Three control variables were also introduced in the regression analysis, in
order to test the robustness of the model. The first one reflects the respondent’s role: it divides the
sample in people who are directly part of the community (e.g., players, coaches and managers)
and people who are outside the community (fans and players’ relatives). The second control
variable refers to the frequency of respondents’ attendance at the team’s matches. Based on this
frequency, a score between 1 and 10 was given to the respondents as follows: “every time the
team plays” = 10; “once a week” = 9; “once a month” = 4; “when I can” = 2; “never” = 0. The
third control variable reflects the frequency of the respondent’s website visits. The responses
were recoded as follows: “everyday” = 10; “once a week” = 7; “once a month” = 3. Even if the
recoding of these control variables is made on an arbitrary basis, we presume that, in this case,
the recoded variables give us the chance to consider the different levels of distance between the
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response options. This means that the “distance” between “once a week” and “once a month” in
the frequency of match attendance should not be equal to the same distance in the frequency of
website visiting (considering that the frequency of matches is usually once a week). A
preliminary analysis of data confirmed the pertinence of the recoding. The comparison with the
analysis developed using dummies obtained from the original variables (rather than with recoded
ones) supports the hypothesis that the recoded variables are more effective in highlighting the
relationships studied by the model.
Before the analysis, all of the factors were tested for multicollinearity, but the results did not
reveal problems (VIF < 1.479). The correlations between the tested variables are shown in Table
Table 3 illustrates the results of the regression analysis (standardised regression coefficients (B),
standard errors, and VIFs). Both the R2 (.679) and the adjusted R2 (.469) are significantly
different from zero (p < .000). To validate the regression analysis, the underlying assumptions
were tested: the analysis of the normal probability plot of residuals and the plot of the residuals
against the predicted values confirm the hypotheses of normal distribution and of
homoscedasticity.
The results of the regression analysis show that all the six considered variables (attitude towards
online sponsors, goodwill towards online sponsors, correspondence between sponsors and
communities, commitment to the community, peer-to-peer communication by email and peer-to-
peer communication by posts on social networks) are significant, except one (viral
communication by email). The model accounts for approximately 46% of the variance in the
intention to purchase of online sponsoring products.
These findings support the following five of the six suggested hypotheses: commitment to the
community (hp. 1), peer-to-peer communication in social networks (hp. 3), attitude towards
online sponsors (hp. 4), goodwill towards online sponsors (hp. 5), and correspondence between
sponsors and communities (hp. 6).
These results are interesting for three reasons. First, in an online community sponsorship predicts
the intention to purchase the sponsoring products and brands, as expected. In addition, the
characteristics of online community members (commitment and peer-to-peer communication) can
directly affect their intention to purchase sponsoring products. The third interesting element
pertains to the role of viral communication: our analysis indicates that not all collectors
demonstrate a greater intention to purchase sponsoring products than passive members. People
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active in social networks are more sensitive to sponsorship, but people who use personalised tools
(such as email) do not show a significantly higher level of intention to purchase online.
Therefore, online long-tail communities can be a source of committed people who also
participate actively in other social networks. Collectors in these virtual contexts can leverage
sponsoring products, thereby becoming viral communication diffusers.
Finally, peer-to-peer communication by email is not significant; nevertheless, the sign of the beta
parameter leads us to presume a negative relation. Although more analysis is needed, this initial
counterintuitive signal suggests that not all active community members are equal. Some members
create value and are more receptive to sponsorship messages, whereas other active members may
not contribute to the effectiveness of sponsorship.
Table 3: Regression model (dependent variable: intention to purchase online sponsor’s products)
Standardized coefficients
(beta)
Standard error VIF Hypothesis check
Commitment to the community .228* .068 1.146 H1 supported
Peer-to-peer communication by email -.126 .168 1.479 H2 not supported
Peer-to-peer communication by posts on social networks .244* .163 1.348 H3 supported
Attitude towards online sponsors .312* .072 1.051 H4 supported
Goodwill towards online sponsors .267* .071 1.045 H5 supported
Congruence between sponsors and communities .361* .075 1.047 H6 supported
Role on the team 0.34 .025 1.171 -
Frequency of match attendance -0.35 .029 1.101 -
Frequency of website visits 0.53 .027 1.117 -
R2 = 0.679*; adjusted-R2 = 0.461*
* p < .05
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6 Conclusions
Sport sponsorship has become an increasingly important element of a firm’s communication mix,
with many corporations actively pursuing sponsorship in an attempt to avoid the clutter
associated with more traditional marketing communications (Meenaghan, 1996).
With sport sponsorship, sports spectators are exposed to corporate messages under favourable
conditions such as enthusiasm, excitement and enjoyment, which make them more relaxed and
receptive to the promotional message (Dolphin, 2003). In the same way, in online communities
pertaining to personal hobbies, members are exposed to sponsorship in a favourable condition of
participation and concentration.
Thus, our results demonstrate that, in online contexts, sponsorship affects the attitude, goodwill
and perceived congruence towards sponsoring brands and product, factors which together also
predict behavioural outcomes (the intention to purchase).
In addition, we also identify two important characteristics of online communities that can
leverage the sponsorship effectiveness: members’ commitment and peer-to-peer communication.
The results of our analysis support the assertion that in niche communities, members’
commitment is a direct antecedent to their intention to purchase online sponsoring products and
services. Because the members of these communities are directly involved in the existence and
development of the community itself, they likely understand very well the benefits that sponsors
can provide to their activities; for these reasons, they demonstrate a higher responsiveness to
sponsoring brands and products.
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These findings are particularly useful for brand managers because, on the whole, contemporary
consumers are less committed to brands (Firat and Venkatesh, 1993), and people are also losing
interest in advertising. Meanwhile, the time they are spending online is increasing each year, and
the cost of online contact is very low. For these reasons, online long-tail groups can become a
lucrative market for sponsorships. In these contexts, companies and members can share value in a
more cooperative way, transferring the feelings and the messages that buying sponsors’ products
also means supporting the team.
Furthermore, the results demonstrate not only the strength of commitment to a group in these
communities, but also the potential for peer-to-peer communication to trigger online viral
communication among members participating in different social networks. The Internet is an
open ecosystem (Hanna et al., 2011) where peer communication is easy and costless. Thus,
people engaged in collecting and sharing information (collectors) can also be bearers of
sponsored content and sponsoring brands. Sport community members are also more sensitive to
sponsorship, showing a higher level of intention to purchase of online sponsoring products than
more passive members. This means that they also carry positive messages in the virtual
ecosystem.
These results are very valuable for brand managers because, according to Bernoff and Li (2008),
collectors are different from creators (members who publish, maintain and upload), critics
(members who comment and rate information and news), joiners (members who connect and
unite) and spectators (passive members). Collectors mostly use online functions like “share this
news on Facebook” or “tweet this information”, and marketing managers can monitor them
simply using some web metrics.
Therefore, this research confirms the general applicability of the model suggested by Meenaghan
(2001b) in an online community context. Moreover, a further contribution to the literature is the
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demonstration that online communities can become a more receptive field for sponsorship due to
the members’ commitment and their activities in peer communication.
This study has limitations as well. Because it is exploratory research, further analyses with
different samples are needed before generalising the results. In addition, this empirical study
focuses on a particular community of interests related to sport activities, and thus, future analysis
could extend to different communities of interests (e.g., several kinds of hobbies). Finally, the
number of participants surveyed should be increased.
Despite these limitations, this study generates several research opportunities regarding online
communities, their members’ behaviour and their efficacy from the perspective of sponsoring
companies.
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