Research on how a brand social media account's network position impacts engagement and reach. Findings were published in Communication Research Reports.

1. UofL Design and Print
ResultsBackground
Methods
Conclusions
Select References
Online Brand Communities as Networks:
The Role of Interactivity and Network Centrality on Engagement and Reach
The analyses support the argument that social media accounts should not be
treated as individual “communities” but rather considered as single point of contact
for the brand embedded within a larger brand network. The practical implications
of this study are also unexpected and novel; namely when a brand interacts with
itself it may be able to effectively boost user engagement and, potentially expand
it’s reach. However, caution should be used not to overstate the findings of this
study; regression analyses demonstrate relationships but not causation.
1. Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer Engagement: Conceptual Domain,
Fundamental Propositions, and Implications for Research. Journal of Service Research, 14(3), 252-271.
2. Schivinski, B., & Dakabrowski, D. (2016). The effect of social media communication on consumer
perceptions of brands. Journal of Marketing Communications, 22 (2), 189-214.
3. Dessart, L., Veloutsou, C., & Morgan-Thomas, A. (2015). Consumer engagement in online brand
communities: a social media perspective. Journal of Product & Brand Management, 24(1), 28-42.
4. Labrecque, L. I. (2014). "Fostering Consumer–Brand Relationships in Social Media Environments: The
Role of Parasocial Interaction." Journal of Interactive Marketing, 28(2): 134-148.
Table 1. Testing the Contributions of Brand Interactivity and Brand Network Position on
Engagement and Reach.
A hierarchical regression analysis was conducted to assess whether variance in
an accounts’ reach could be explained by interactions between brand accounts
after controlling for interactivity with consumers. Results showed that interactivity
between brand accounts explained a significant amount of variance above that
explained by interactions with users, R2 =.13, F(1, 3158) = 79.07, p < .001). A
second analysis tested whether an account’s centrality within a brand’s retweet
and addressivity networks would explain additional variance. Indeed, the centrality
of the account was significant and was positively associated with reach, R2 =.29,
F(2, 3156) =365.26, p < .001.
Hierarchical regression analyses were conducted to evaluate whether the number
of likes and retweets, both measures of engagement, were predicted by
interactions among brand accounts after controlling for interactivity with
consumers and the number of account followers. Results indicated that there was
a significant increase in the proportion of explained variance (for retweets: R2 =
.46, F(1, 3125) = 317.99, p < .001; for likes: R2 =.70, F(1, 3125) =55.70, p < .001).
This suggests that when brand accounts interact with one another they are able to
encourage user engagement.
A second analysis was conducted to determine whether an accounts centrality
within a brand network would explain additional variance for the engagement
measures. Results indicated that centrality in the network was a significant
predictor (for retweets : R2 = .51, F(1, 3123) = 140.54, p < .05; for likes: R2 =.71,
F(2, 3123) =9.47, p < .001 ), suggesting that the position in the network of brand
accounts explained engagement by consumers beyond that which could be
explained by interactivity.
It is common for brands to have multiple social media accounts not only on
different social media platforms but also on a single social media outlet. Users may
not experience brand’s social media accounts as discrete communities but only as
points of contact with the brand.
Engagement is a “psychological state that occurs by virtue of interactive, co-
creative customer experiences with a focal agent/object (e.g. a brand)“1
.
Consumer engagement is an antecedent to purchase intentions2
, fosters brand
loyalty3
, and the willingness to share the brands social media content4
.
Interactions between brand social media accounts may contribute to user
engagement and reach by:
• Creating a human conversational tone that promotes consumer bonding
and identification with the brand.
• Conferring credibility to lesser-known accounts when interacting with well-
known accounts.
• Creating hyperlinks that allow consumers to find other brand accounts.
Purpose of the Study. The purpose of this study is to test whether interaction
between brand social media accounts is related to user engagement and
expanded reach (i.e. number of followers).
Data Collection. The Twitter API was queried for accounts whose screen name
incorporated brand names found on the 2014 Interbrand’s Top 100 Global Brands
list.
• All non-verified brands were deleted from the results.
• A researcher screened the remaining accounts for false positives.
• A snowball sample was conducted to find non-verified brand accounts by
searching for accounts with brand names in their screen names that had
been retweeted or followed by a known brand account.
• Tweets were downloaded for each account (maximum 4000).
Measures.
• User Interactivity. Composite of the number of times a brand replies to,
mentions, or retweets accounts belonging to users.
• Brand Interactivity. Composite of the number of times a brand replies to,
mentions, or retweets accounts belonging to the brand.
• In-degrees of Addressivity Network. In-degree for an account from the
reply/mention network.
• Out-Degree of Retweet Network. Out-degree for an account from the
retweet network.
Data Analysis. A hierarchical linear regression was conducted to determine if
brand interactivity and network measures accounted for additional variance above
and beyond control variables.
W. Scott Sanders, Ph.D.
Department of Communication
University of Louisville
Qi Zheng, Ph.D.
Department of Bioinformatics and Biostatistics
University of Louisville
Jasmine Wang, Ph.D.
Departments of Communication
University of Louisville
Figure 2. Example Brand Network:
Nike Addressivity Network
Model R2
F Δ R2
Reach Retweets Likes Reach Retweets Likes Reach Retweets Likes
1 .11 .41 .70 384.22** 1080.44** 3636.41** .11 .41 .70
2 .13 .46 .71 79.08** 317.99** 55.70** .02 .06 .01
3 .29 .51 .71 365.26** 140.55** 9.47** .16 .04 .002
Note: ** p< .001
Model B SE B β
Reach Rtwts Likes Reach Rtwts Likes Reach Rtwts Likes
No. of Followers - .52** .75** - .01 .01 - .56 .75
User Interactivity .58** .27** .21** .03 .03 .02 .41 .19 .14
Brand Interactivity -.33** .31** .28** .05 .04 .03 -.18 .17 .15
Out-Degree Retweets -.54** .57** -.12** .05 .04 .04 -.24 .27 -.06
In-Degree Addressivity .91** -.41** -.02 .03 .03 .03 .46 -.22 -.01
Note: ** p< .001
Table 2. Parameter Estimates for Model 3
0 50 100 150 200
Microsoft
Sony
Google
IBM
MTV
Disney
Samsung
Nike
Cisco
Oracle
Intel
Ford
GE
Adobe
Verified Unverified
Figure 1. Brands with the
Most Twitter Accounts
Aleeza Gardner
Department of Communication
University of Louisville