Retweeting service experiences: factors influencing receivers to proliferate brand image disruptions

Research output: Contribution to conferenceAbstract

Abstract

The advent of customers sharing their positive and negative experiences of service encounters on social media sites such as Twitter, and the viral nature of these shared experiences, has received a tremendous amount of media coverage over the last few years. With regard to negative brand experiences, one of the most memorable events in recent history was a video of Dr. David Dao being forcibly removed from a United Airlines flight for refusing to give up his seat due to an overbooked flight. The event was initially filmed and shared on Twitter and subsequently sparked global outrage with over 100 million views of the video in China alone. Although this example is considered extreme in nature and garnered the media attention it deserved, little is known about the receivers of these shared service brand experiences, termed MeWOM brand image disruptions in this study, and the factors that motivate them to retweet them. MeWOM brand image disruptions are defined as eWOM in a microblog that either positively promotes the course, progress or transmission of a brand’s image, or eWOM in a microblog that interrupts the course, progress, or transmission of a brand’s image. Through the use of mobile technology such as smart phones and the platforms that host social network sites, consumers share their daily positive and negative experiences about airlines just as they would tell a close friend or next-door neighbor in the past. Only now, they tell the world about their service encounters and do so through the use of a range of formats including text, photographs and videos. There is however a stark dearth of empirical data to help support managers in their understanding of the factors that influence the proliferation of shared brand experiences in the brief moments after receivers are exposed to them on microblogs such as Twitter. This study seeks to fill this gap in knowledge by focusing on a seemingly overlooked important actor in the microblog domain, receivers of MeWOM brand image disruptions and their propensity to share them with others. 372 Twitter users in the United States were exposed to six positive and negative MeWOM brand image disruptions relating to airlines in an online experiment that replicated the Twitter environment. Two PLS Structural Equation Models were created to determine the factors that motivated receivers to retweet the MeWOM brand image disruptions. The results demonstrated that corporate reputation after exposure, message relevance and issue involvement were predictors in both the negative and positive valence models. One emotion, surprise was also a significant predictor in the positive valence model only.
LanguageEnglish
Number of pages1
Publication statusPublished - 8 Sep 2018
EventFrontiers in Service 2018 - Texas State University, Austin, United States
Duration: 6 Sep 20189 Sep 2018

Conference

ConferenceFrontiers in Service 2018
CountryUnited States
CityAustin
Period6/09/189/09/18

Fingerprint

flight
social network
Service experience
Brand image
Influencing factors
Twitter
Disruption
services
photograph
Airlines
Brand experience
history
video
Service encounter
Factors
Electronic word-of-mouth
Valence
Predictors
experiment
China

Keywords

  • Twitter
  • brand
  • reputation

Cite this

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title = "Retweeting service experiences: factors influencing receivers to proliferate brand image disruptions",
abstract = "The advent of customers sharing their positive and negative experiences of service encounters on social media sites such as Twitter, and the viral nature of these shared experiences, has received a tremendous amount of media coverage over the last few years. With regard to negative brand experiences, one of the most memorable events in recent history was a video of Dr. David Dao being forcibly removed from a United Airlines flight for refusing to give up his seat due to an overbooked flight. The event was initially filmed and shared on Twitter and subsequently sparked global outrage with over 100 million views of the video in China alone. Although this example is considered extreme in nature and garnered the media attention it deserved, little is known about the receivers of these shared service brand experiences, termed MeWOM brand image disruptions in this study, and the factors that motivate them to retweet them. MeWOM brand image disruptions are defined as eWOM in a microblog that either positively promotes the course, progress or transmission of a brand’s image, or eWOM in a microblog that interrupts the course, progress, or transmission of a brand’s image. Through the use of mobile technology such as smart phones and the platforms that host social network sites, consumers share their daily positive and negative experiences about airlines just as they would tell a close friend or next-door neighbor in the past. Only now, they tell the world about their service encounters and do so through the use of a range of formats including text, photographs and videos. There is however a stark dearth of empirical data to help support managers in their understanding of the factors that influence the proliferation of shared brand experiences in the brief moments after receivers are exposed to them on microblogs such as Twitter. This study seeks to fill this gap in knowledge by focusing on a seemingly overlooked important actor in the microblog domain, receivers of MeWOM brand image disruptions and their propensity to share them with others. 372 Twitter users in the United States were exposed to six positive and negative MeWOM brand image disruptions relating to airlines in an online experiment that replicated the Twitter environment. Two PLS Structural Equation Models were created to determine the factors that motivated receivers to retweet the MeWOM brand image disruptions. The results demonstrated that corporate reputation after exposure, message relevance and issue involvement were predictors in both the negative and positive valence models. One emotion, surprise was also a significant predictor in the positive valence model only.",
keywords = "Twitter, brand, reputation",
author = "Jennifer Barhorst and Alan Wilson",
year = "2018",
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language = "English",
note = "Frontiers in Service 2018 ; Conference date: 06-09-2018 Through 09-09-2018",

}

Retweeting service experiences: factors influencing receivers to proliferate brand image disruptions. / Barhorst, Jennifer; Wilson, Alan.

2018. Abstract from Frontiers in Service 2018, Austin, United States.

Research output: Contribution to conferenceAbstract

TY - CONF

T1 - Retweeting service experiences: factors influencing receivers to proliferate brand image disruptions

AU - Barhorst, Jennifer

AU - Wilson, Alan

PY - 2018/9/8

Y1 - 2018/9/8

N2 - The advent of customers sharing their positive and negative experiences of service encounters on social media sites such as Twitter, and the viral nature of these shared experiences, has received a tremendous amount of media coverage over the last few years. With regard to negative brand experiences, one of the most memorable events in recent history was a video of Dr. David Dao being forcibly removed from a United Airlines flight for refusing to give up his seat due to an overbooked flight. The event was initially filmed and shared on Twitter and subsequently sparked global outrage with over 100 million views of the video in China alone. Although this example is considered extreme in nature and garnered the media attention it deserved, little is known about the receivers of these shared service brand experiences, termed MeWOM brand image disruptions in this study, and the factors that motivate them to retweet them. MeWOM brand image disruptions are defined as eWOM in a microblog that either positively promotes the course, progress or transmission of a brand’s image, or eWOM in a microblog that interrupts the course, progress, or transmission of a brand’s image. Through the use of mobile technology such as smart phones and the platforms that host social network sites, consumers share their daily positive and negative experiences about airlines just as they would tell a close friend or next-door neighbor in the past. Only now, they tell the world about their service encounters and do so through the use of a range of formats including text, photographs and videos. There is however a stark dearth of empirical data to help support managers in their understanding of the factors that influence the proliferation of shared brand experiences in the brief moments after receivers are exposed to them on microblogs such as Twitter. This study seeks to fill this gap in knowledge by focusing on a seemingly overlooked important actor in the microblog domain, receivers of MeWOM brand image disruptions and their propensity to share them with others. 372 Twitter users in the United States were exposed to six positive and negative MeWOM brand image disruptions relating to airlines in an online experiment that replicated the Twitter environment. Two PLS Structural Equation Models were created to determine the factors that motivated receivers to retweet the MeWOM brand image disruptions. The results demonstrated that corporate reputation after exposure, message relevance and issue involvement were predictors in both the negative and positive valence models. One emotion, surprise was also a significant predictor in the positive valence model only.

AB - The advent of customers sharing their positive and negative experiences of service encounters on social media sites such as Twitter, and the viral nature of these shared experiences, has received a tremendous amount of media coverage over the last few years. With regard to negative brand experiences, one of the most memorable events in recent history was a video of Dr. David Dao being forcibly removed from a United Airlines flight for refusing to give up his seat due to an overbooked flight. The event was initially filmed and shared on Twitter and subsequently sparked global outrage with over 100 million views of the video in China alone. Although this example is considered extreme in nature and garnered the media attention it deserved, little is known about the receivers of these shared service brand experiences, termed MeWOM brand image disruptions in this study, and the factors that motivate them to retweet them. MeWOM brand image disruptions are defined as eWOM in a microblog that either positively promotes the course, progress or transmission of a brand’s image, or eWOM in a microblog that interrupts the course, progress, or transmission of a brand’s image. Through the use of mobile technology such as smart phones and the platforms that host social network sites, consumers share their daily positive and negative experiences about airlines just as they would tell a close friend or next-door neighbor in the past. Only now, they tell the world about their service encounters and do so through the use of a range of formats including text, photographs and videos. There is however a stark dearth of empirical data to help support managers in their understanding of the factors that influence the proliferation of shared brand experiences in the brief moments after receivers are exposed to them on microblogs such as Twitter. This study seeks to fill this gap in knowledge by focusing on a seemingly overlooked important actor in the microblog domain, receivers of MeWOM brand image disruptions and their propensity to share them with others. 372 Twitter users in the United States were exposed to six positive and negative MeWOM brand image disruptions relating to airlines in an online experiment that replicated the Twitter environment. Two PLS Structural Equation Models were created to determine the factors that motivated receivers to retweet the MeWOM brand image disruptions. The results demonstrated that corporate reputation after exposure, message relevance and issue involvement were predictors in both the negative and positive valence models. One emotion, surprise was also a significant predictor in the positive valence model only.

KW - Twitter

KW - brand

KW - reputation

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M3 - Abstract

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