Business engagement on Twitter: a path analysis

Social media services, such as Twitter, enable commercial businesses to participate actively in online word-of-mouth communication. In this project, we examined the potential influences of business engagement in online word-of-mouth communication on the level of consumers’ engagement and investigated the trajectories of a business’ online word-of-mouth message diffusion in the Twitter community. We used path analysis to examine 164,478 tweets from 96,725 individual Twitter users with regards to nine brands during a 5-week study period. We operationalized business engagement as the amount of online word-of-mouth messages from brand and the number of consumers the brand follows. We operationalized consumers’ engagement as the number of online word-of-mouth messages from consumers both connecting to the brand and having no connection with the brand as well as the number of consumers following the brand. We concluded that the business engagement on Twitter relates directly to consumers’ engagement with online word-of-mouth communication. In addition, retweeting, as an explicit way to show consumers’ response to business engagement, indicates that the influence only reaches consumers with a second-degree relationship to the brand and that the life cycle of a tweet is generally 1.5 to 4 hours at most. Our research has critical implications in terms of advancing the understanding of the business’s role in the online word-of-mouth communication and bringing insight to the analytics of social networks and online word-of-mouth message diffusion patterns.

[1]  Peeter W. J. Verlegh,et al.  The Firm's Management of Social Interactions , 2005 .

[2]  E. Keller Unleashing the Power of Word of Mouth: Creating Brand Advocacy to Drive Growth , 2007, Journal of Advertising Research.

[3]  M. C. Ortiz,et al.  Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes , 2004 .

[4]  Vijay Mahajan,et al.  The Economic Leverage of the Virtual Community , 2001, Int. J. Electron. Commer..

[5]  Barbara M. Byrne,et al.  Structural equation modeling with EQS : basic concepts, applications, and programming , 2000 .

[6]  Bernard J. Jansen,et al.  Twitter power: Tweets as electronic word of mouth , 2009, J. Assoc. Inf. Sci. Technol..

[7]  Tony Adams,et al.  Book Review: How Brands Become Icons: The Principles of Cultural Branding , 2005, Journal of Advertising Research.

[8]  Dom Sagolla 140 Characters: A Style Guide for the Short Form , 2009 .

[9]  Deepak Khazanchi,et al.  An Empirical Study of Online Word of Mouth as a Predictor for Multi-product Category e-Commerce Sales , 2008, Electron. Mark..

[10]  L de Chernatony Succeeding with brands on the Internet , 2001 .

[11]  David K. Perry,et al.  Viral Marketing or Electronic Word-of-Mouth Advertising: Examining Consumer Responses and Motivations to Pass Along Email , 2004, Journal of Advertising Research.

[12]  Cynthia M. Webster,et al.  Word-Of-Mouth Communications: a Motivational Analysis , 1998 .

[13]  George Fisk,et al.  Word of Mouth Advertising , 1969 .

[14]  Moses E. Olobatuyi A User's Guide to Path Analysis , 2006 .

[15]  Dwayne D. Gremler,et al.  Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? , 2004 .

[16]  Robert M. Schindler,et al.  Internet forums as influential sources of consumer information , 2001 .

[17]  Peter Tarasewich,et al.  Global perceptions of journals publishing e-commerce research , 2002, CACM.

[18]  D. F. Blankertz,et al.  Risk taking and information handling in consumer behavior , 1969 .

[19]  Hong-Youl Ha Factors influencing consumer perceptions of brand trust online , 2004 .

[20]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .