Electronic Markets

, 21:161 | Cite as

Business engagement on Twitter: a path analysis

  • Mimi ZhangEmail author
  • Bernard J. Jansen
  • Abdur Chowdhury
Special Theme


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.


Twitter Social network Electronic word-of-mouth Advertising Information diffusion 


M3—Business administration and business economics Marketing Accounting—Marketing and advertising 


  1. Arndt, J. (1967). Word-of-mouth advertising and informal communication. In D. F. Cox (Ed.), Risk taking and information handling in consumer behavior (pp. 188–239). Cambridge: Harvard University Press.Google Scholar
  2. Balasubramanian, S., & Mahajan, V. (2001). The economic leverage of the virtual community. International Journal of Electronic Commerce, 5(3), 103–138.Google Scholar
  3. Bharati, P., & Tarasewich, P. (2002). Global perceptions of journals publishing e-commerce research. Communications of the ACM, 45(5), 21–26.CrossRefGoogle Scholar
  4. Bickart, B., & Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of Interactive Marketing, 15(3), 31–40.CrossRefGoogle Scholar
  5. Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). New York: Routledge.Google Scholar
  6. Davis, A., & Khazanchi, D. (2008). An empirical study of online word of mouth as a predictor for multi-product category e-Commerce sales. Electronic Markets, 18(2), 130–141.CrossRefGoogle Scholar
  7. de Chernatony, L. (2000). Succeeding with brands on the Internet. Brand Management, 8(3), 186–195.CrossRefGoogle Scholar
  8. Godes, D., Mayzlin, D., Chen, Y., Das, S., Dellarocas, C., Pfeiffer, B., et al. (2005). The firm’s management of social interactions. Marketing Letters, 16(3/4), 415–428.Google Scholar
  9. Ha, H.-Y. (2004). Factors influencing consumer perceptions of brand trust online. The Journal of Product and Brand Management, 13(5), 329–342.CrossRefGoogle Scholar
  10. Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18(1), 38–52.CrossRefGoogle Scholar
  11. Holt, D. B. (2004). How brands become icons: The principles of cultural branding. Boston: Harvard Business School.Google Scholar
  12. Jansen, B. J., Zhang, M., Sobel, K., & Chowdhury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science, 60(11), 2169–2188.CrossRefGoogle Scholar
  13. Keller, E. (2007). Unleashing the power of word of mouth: Creating brand advocacy to drive growth. Journal of Advertising Research, 47(4), 448–452.CrossRefGoogle Scholar
  14. Kling, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford Press.Google Scholar
  15. Lleti, R., Ortiz, M. C., Sarabia, L. A., & Sanchez, M. S. (2004). Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes. Analytica Chimica Acta, 515(1), 87–100.CrossRefGoogle Scholar
  16. Olobatuyi, M. E. (2006). A user’s guide to path analysis. Lanham: University Press of America, Inc.Google Scholar
  17. Phelps, J. E., Lewis, R., Mobilio, L., Perry, D., & Raman, N. (2004). Viral marketing or electronic word-of-mouth advertising: Examining consumer responses and motivations to pass along email. Journal of Advertising Research, 44(4), 333–348.Google Scholar
  18. Sagolla, D. (2009). 140 characters: A style guide for the short form. Hoboken: Wiley.Google Scholar
  19. Sundaram, D. S., Mitra, K., & Webster, C. (1998). Word-of-mouth communications: A motivational analysis. Advances in Consumer Research, 25(1), 527–531.Google Scholar
  20. Wauters, R. (2009). Twitter spawned 50,000 apps to date, will open up firehose for more. Retrieved January 6, 2010, from

Copyright information

© Institute of Information Management, University of St. Gallen 2011

Authors and Affiliations

  • Mimi Zhang
    • 1
    Email author
  • Bernard J. Jansen
    • 1
  • Abdur Chowdhury
    • 2
  1. 1.College of Information Sciences and TechnologyThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.TwitterSan FranciscoUSA

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