Factors influencing popularity of branded content in Facebook fan pages

Factors influencing popularity of branded
content in Facebook fan pages
Sabate, F., Berbegal-Mirabent, J., Cañabate, A., & Lebherz, P. R.

Our research in digital marketing
 Sabate, F. (2005). Eficiencia de mercado y el canal internet. Estudio empírico del mercado de CDs de música
en Barcelona. Director Tesis Doctoral: Cañabate, A.
• Sabate, F., Cañabate, A., & Sole, F. (2004). ¿ Son los mercados de Internet más eficientes? Resumen de las últimas
evidencias empíricas. Intangible Capital.
• Sabate, F.; Cañabate, A. (2007). Mercados minoristas en Internet: distinto canal, ¿igual eficiencia? Book of Abstracts,
Congreso de Ingeniería de Organización
• Sabate, F., Cañabate, A., Cobo, E., & Garcia, D. (2009). Is the Internet making markets more efficient? The evidence
according to price indicators in Spain. Intangible Capital.
 Sicilia, C.; Sabate, F.; Sallán, J.M. (2008). Evaluando la marca en las redes sociales y Web 2.0. XII Congreso
de Ingeniería de Organización.
 Cañabate, A.; Causí, A.; Sabate, F.; Consolación, C. (2009). El estado del arte de las estrategias de
promoción de Marketing Móvil. XIII Congreso de Ingeniería de Organización.
 Sabate, F.; Arjones, R.; Cañabate, A.; Consolación, C. (2009). Estrategias SEM de pago por clic en el sector
de las floristerías. XIII Congreso de Ingeniería de Organización.
 Sabate, F., Berbegal, J., Consolación, C., & Cañabate, A. (2009) SEO strategies in booksellers sector.
Intangible Capital.
 Sabate, F., Canabate, A., Velarde-Iturralde, M.-A., & Grinon-Barcelo, R. (2010) Use of internet promotion
strategies by the Spanish travel agencies. Profesional de la Información.
 Sabate, F., Berbegal-Mirabent, J., Cañabate, A., & Lebherz, P. R. (2014). Factors influencing popularity of
branded content in Facebook fan pages. European Management Journal.
 Sabate, F., Canabate, A., & Fonseca, P. Review and analysis of the SEO factors. (en preparación)
July 1st, 2014 Factors influencing popularity of branded content in Facebook fan pages 2

Digital marketing
Attraction
Loyalty
Conversion
* *
Branding Recommendation
*
* Social Networking Sites communication tactics

Social Networking Sites (SNS)

Social Networking Sites (SNS)
 Among others, benefits arising from a well-designed social media marketing strategy:
• better grasp of consumers’ behaviors and preferences
• making consumers share the brand’s message as word of mouth to their peers
• connecting to consumer for improvement and R&D processes
• increasing brand engagement and brand message exposure
• as well as driving traffic to corporate websites
(Hettler, 2010; Smith & Zook, 2011; Tuten, 2008).
 Several studies confirm the influence of user generated content on purchase
intention
(Chevalier & Mayzlin, 2006; Dhar & Chang, 2009; Duan, Gu, & Whinston, 2008;
Rehmani & Khan, 2011; Sierra Sánchez, 2012; Ye, Law, & Gu, 2009).
 Social media marketing should be oriented:
1. to understand clients
2. to create custom-made online content
3. to define a fitting strategy in a way that strengthens the reputation of the brand.
(Hettler, 2010; Heymann-Reder, 2011; Kilian & Langner, 2010)
 Which characteristics should have this online content in order to be spread by
consumers, just because people like to share the content to their?
 Content is the instrument that stimulates interaction. Brands must publish pieces of
content trying to address customers’ motivations delivering interesting content for
them when and where needed.

Facebook fan (brand) pages
 Dissemination of branded content or posts can be achieved through several
mechanisms.
• Users who are fans of the brand will see in their walls this branded content, and then they
can interact with it by liking, sharing or commenting.
• Each of these actions potentially promotes the content to all the customer’s friends’ walls.
• Consequently friends of fans can also contribute to exponentially disseminate this content.

 There are no theoretical frameworks available yet that could be used to analyze why
and how users contribute to social media.
Singh, Jain, and Kankanhalli (2011)
 There is no simple formula that guides on how to publish in social media due to
diversity of brands’ goals and sectorial characteristics.
 Consequently, we focus our analysis:
(Agresta & Bough, 2011)
• on the Spanish travel agencies sector
• by carrying out an empirical study of the posts published by five Spanish travel agencies on
their Facebook fan pages
• and the users’ interaction with them.
Theoretical background

Theoretical background.
Content attributes
 Soft criterion: considers the semantics and the interpretation of the message behind a post
• Posts revealing funny things of the working environment, news affecting the business or
information that may report direct economic benefits to the reader are more prone to
capture user’s attention.
Heymann-Reder (2011) and Hettler (2010)
• Post category has a significant effect over the user interaction.
(aDigital, 2011; Pletikosa Cvijikj & Michahelles, 2011)
• Difficulty in capturing and processing relevant data for analysis.
 Hard criterion: can be quantified without the need of a subjective interpretation process
• Frequency and timing a phenomenon takes place.
• Richness of the content associated to a post, by simply looking at the content type that
complements the text (i.e. a picture, a video or a link to anotherwebsite).
• Also characteristics of the published content, but are more easily to capture and process.

(Baltas, 2003) Buddy Media Inc. (2011)
Richness (vividness):
Previous studies found that the richness of the
message may play a role.
(Daft & Lengel, 1986) (De Vries, Gensler, & Leeflang,
2012; Pletikosa Cvijikj & Michahelles, 2013) (Fortin &
Dholakia, 2005) Brookes (2010)
In contrast to previous studies where a priori
judgments about progressive levels of richness (low,
medium and high) are assumed, we only
differentiate according to content type (images,
videos and links). This way we avoid any potential
subjective bias on how richness is perceived in the
users.
Theoretical background.
Conceptual Model
(Hong, Dan, & Davison, 2011; Suh,
Hong, Pirolli, & Chi, 2010)
Time Frame:
Day of the week: (working days > weekend)
(Golder, Wilkinson, & Huberman, 2007) Buddy Media
Inc. (2011) Rutz and Bucklin (2011)
Time of publication:
evening > night > morning
early morning & late at night -> engagement rates 20%
higher
Posts created during periods with low user activity ->
probability for being liked or commented is higher (will
appear at the top of the users’ wall in peak hours).
Golder et al. (2007)
Buddy Media Inc. (2011)
Pletikosa Cvijikj and Michahelles (2013)
There is no clear agreement.

 Sample:
• Travel Agencies
o The number of social media channels
(Facebook, Twitter, YouTube and Blogs, …)
o Traffic rank: Alexa
o Economic: accounting for the revenues obtained
during 2008 and 2009
 5: Rumbo.es, Atrapalo.com, eDreams.es,
MuchoViaje.com and Barceloviajes.com
 Data Collection
• Gathered manually over one month
o [March 21st to April 21st, 2011]  Delay 
[February to 21st to March 21st, 2011]
o Optimum Delay
 164 posts were obtained
Methodology

 Variables
Methodology

 Method Alternatives: Dependent Variables are count variables
Methodology
• Multiple OLS linear regressions (STEPWISE, IN:F <= .050; OUT: F >= .100)
o Consider transformations to guarantee a normal distribution of the residual: log-transformations
o Intuitive interpretation: R2 explanatory power of the model
• Negative Binomial Regression Model / Poisson Models
o In our case, better Binomial Regression Model: Overdispersed count data
(conditional variance >>> conditional mean)
o Poisson models are often not observed with social data
o Consider Zero-inflated models: excess zeros
• SEM
o SEM permits the evaluation of direct and indirect effects.
• SEM & Multiple OLS linear regression
o “Our model does not reflect (neither suggests) indirect effects of variables as the literature reviewed
does not provide indications in this direction”
o “Our hypotheses are expressed in terms of the direct effects of the observed independent variables
over the dependent ones (likes and comments). We do not need to instrument our model through
latent variables”
o Useful references: Musil, Jones & Warner (1998) ; Gefen, Straub & Boudreau (2000)

Results
F(4;157) = 48.468 (p-value < 0.001) F(4;159) = 21.828 (p-value < 0.001)

 Likes
• Residuals
o Q–Q plot of standardized residual
o Kolmogorov–Smirnov test (p-value=0.200)
o Shapiro–Wilk test (p-value=0.054)
• Independence
o Durbin–Watson’s test = 1.665  [1.5, 2.5]
• Homoscedasticity
o Equal variance
of d.v. for
all data
• Collinearity
o Maximum VIF index = 1.370 (Characters)
o Problematic if VIF>=10 (rule of thumb) or
VIFs>2.5 (Allison, 1999)
• Statistics
o Expected value of the residuals is 0
o No outlier observations neither critical values
– Standardized residuals  [-2.109, 2.883]
– Maximum value of the Cook’s distance of
the residuals is lower than 1 (0.111)
Results: Model Assumptions
 Comments
• Residuals
o Q–Q plot of standardized residual
o Shapiro–Wilk test (p-value=0.134)
• Independence
o Durbin–Watson’s test = 2.115  [1.5, 2.5]
• Homoscedasticity
o Not clear
o Cameron-Trivedi’s
IM-test
p-value=0.127)
• Collinearity
o Maximum VIF index = 1.721 (Images)
• Statistics
o Expected value of the residuals is 0
o No outlier observations neither critical values
– Standardized residuals  [−2.447, 2.717]
– Maximum value of the Cook’s distance of
the residuals is lower than 1

 Expected and obtained
results by hypotheses
 Discussion (Richness and Time Frame)
Results & Discussion
• Images significantly increase brand post popularity (likes and comments)
• Videos only increase brand post popularity when it is measured through likes
o Consider the complex nature of commenting in comparison with liking
o Images are easier to digest and in a few seconds users can write a short comment about…
o Video requires more time to assimilate content…
• Links are negatively influencing the number of comments
o Following a link implies navigating away from FB to the destination page  risk of not coming back
• Avoiding a priori judgments about progressive levels of richness has been effective
• Posts are published during business hours they are more likely to be commented
o A desktop computer is better to write comments
 people use to be working with desktop computer in business hours
 Outside business hours more users are connecting to Facebook through their mobile terminals

 Discussion (Control Variables)
Discussion & Future Research
• The number of followers significantly increase brand post popularity (likes and comments)
• Supporting the convenience of writing larger posts for increasing the number of likes
o Contradictory results with other studies
> Methodological differences
> Cultural differences
> Industry specificities
 Future Research
• More sophisticated models of brand post popularity by including soft & hard criteria
o SEM: latent variables and indirect casual relationships between factors
o Text mining and Sentiment mining: text, images and videos

Conclusions
 Guidelines for improving the liking of posts published on Facebook brand pages
• Community managers should include images (specially) and videos.
• Moderators should not be worried about writing to many characters if this is essential for a
good understanding of the content
 Guidelines for improving the commenting of posts published on Facebook
• Community managers should include images.
• The avoidance of links would report more comments
• Publishing during business seems to improve users’ willingness to comment
(grain of salt)
 Images are more powerful than videos in increasing consumers’ engagement

Factors influencing popularity of branded content in Facebook fan pages

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