THE DRIVERS AND THE OUTCOMES OF BRAND ENGAGEMENT IN TWITTER CASE
SOCIAL MEDIA
AND BRAND ENGAGEMENT
Background and general relevance
• Establishment of Internet Social media platforms
• “o ial edia is a olle tio of ope -source, interactive, and user-controlled online applications expanding the experiences, knowledge and
arket po er of the users as parti ipa ts i usi ess a d so ial pro esses (Constantinides and Fountain, 2008)
• E gage or die (Nelson-Field and Taylor, 2012) favoring, retweeting
• Brand loyalty: Deep commitment or attachment to a brand or service,
or the desire to buy a certain product or service (Woisetschlager et al., 2008)
• E-word-of- outh: A y positi e or egati e state e t ade y pote tial, actual, or former customers about a product or company, which is made a aila le to a ultitude of people a d i stitutio s ia the I ter et
(Hennig-Thurau et al, 2004)
•
•
•
• Users follow 5 or ore bra ds a d 37% of Twitter users will buy fro a followed bra d
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Twitter Facts and Contribution
Twitter= the most popular microblogging service More than 270 millions
are users-expected growth
63% of brands have multiple Twitter accounts
Users follow 5 or more brands and 37% of Twitter users will buy fro a followed bra d
Interaction with brands is a common theme in social media
usage (Kaplan and Haenlein, 2010) Twitter= the most
interactive social platform The impact of online engagement to
the e-WOM and brand loyalty has been tested for Facebook brand pages
Problem statement and research questions
Purpose: Identify the antecedents of online brand engagement and the possible outcomes of this engagement on Twitter
• Is possible content type (information and entertainment) and brand post characteristics (interactivity and vividness) affect online brand engagement?
• Is e-WOM a possible outcome of this engagement?
• Is brand loyalty a possible outcome of the brand engagement through Twitter?
Conceptual model
Drivers Brand Engagement Outcomes
Brand post Characteristics:
Interactivity vs Vividness
Content type:
Information vs Entertainment
Cognitive Behavioral
Affective
Brand Loyalty E-WOM
Hypotheses
• H1A: Perceived information quality o a T itter ra d’s a ou t that a consumer has follo ed positi ely predi ts cognitive online brand engagement.
• H1B: Perceived information quality o a T itter ra d’s a ou t that a consumer has follo ed positi ely predi ts affective online brand engagement.
• H1C: Perceived information quality o a T itter ra d’s a ou t that a consumer has follo ed positi ely predi ts behavioral online brand engagement.
• H2A: Perceived entertainment o a T itter ra d’s a ou t that a consumer has follo ed positi ely predi ts cognitive online brand engagement.
• H2B: Perceived entertainment o a T itter ra d’s a ou t that a consumer has follo ed positi ely predi ts affective online brand engagement.
• H2C: Perceived entertainment o a T itter ra d’s a ou t that a consumer has follo ed positi ely predi ts behavioral online brand engagement
Hypotheses ii
• H3A: Perceived interactivity o a T itter ra d’s a ou t that a consumer has follo ed positi ely predicts cognitive online brand engagement.
• H3B: Perceived interactivity o a T itter ra d’s a ou t that a consumer has follo ed positi ely predicts affective online brand engagement.
• H3C: Perceived interactivity o a T itter ra d’s a ou t that a consumer has follo ed positi ely predicts behavioral online brand engagement.
• H4A: Perceived vividness o a T itter ra d’s a ou t that a o su er has follo ed positi ely predicts cognitive online brand engagement.
• H4B: Perceived vividness o a T itter ra d’s a ou t that a consumer has follo ed positi ely predicts affective online brand engagement.
• H4C: Perceived vividness o a T itter ra d’s a ou t that a o su er has follo ed positi ely predicts behavioral online brand engagement.
Hypotheses iii
• H5A: Online brand engagement ith a ra d follo ed i Twitter predicts positively loyalty by the follower.
• H5B: Online brand engagement ith a ra d follo ed i T itter predicts positively e-WOM by the follower.
Methodology and measurements
Online survey –distributed through social media
Measurement construct Literature Scale Information quality Cao et al., 2005; Ou & Sia,
2010; Zhang & von Dran, 2000
7 point
Entertainment Cao et al. and Koufaris (2002)
7 point
Interactivity Johnson, Bruner, and Kumar
(2006)
7 point
Vividness Self-constructed 7 point
Online brand engagement
Cognition Cheung et al. (2011), 7 point
Affect Cheung et al. (2011) 7 point
Behavioral Casalo et al. (2010) 7 point
Outcomes
Brand loyalty Chaudhuri & Holbrook, 2001 7 point
E-wom by Zeithaml, Berry &
Parasuraman (1996).
7 point
Data overview
[CATEGORY NAME]
[PERCENTAGE]
[CATEGORY NAME]
[PERCENTAGE]
Total: 276 respondents
(after deletion incomplete + invalid cases)
N=132
Female Male
Principally age category:
18-24 years
Hours spent on Twitter:
M=7.75
Twitter experience level:
Intermediate level M=4.5
(scale 1-7 very experience)
Number of brands
followed on Twitter=20
Model of analysis
Factor analysis resulted in two dimensions of engagement Multiple regression analysis to measure the effects
Independent Variable(s) Dependent
Interactivity Cognitive brand engagement
Vividness Cognitive brand engagement
Information Cognitive brand engagement
Entertainment Cognitive brand engagement
Interactivity Behaff brand engagement
Vividness Behaff brand engagement
Information Behaff brand engagement
Entertainment Behaff brand engagement
Online brand engagement Loyalty
Online brand engagement E-wom
