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Shaping Interactive Marketing Communication (IMC) Through Social Media Analytics and Modelling

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AI 2016: Advances in Artificial Intelligence (AI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9992))

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Abstract

Social media marketing represents a dynamic field for intense research. However, existing researches have not evidently estimated the materiality of the information circulated on Social Media regarding the benefits of business entities. In this work, “Online consumer’s behavior” will be analyzed using Artificial Intelligence (AI) for classificatory modelling through AISAS model by applying the family of Bayesian Classifiers. This study would help marketers in understanding key drivers of the perception towards their impact on the benefits, and enable users of such data to identify triggers that are worth monitoring and investing in.

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References

  1. Zocial Inc., May 2014. http://www.zocialinc.com/

  2. Sugiyama, K.A.T.: The Dentsu way: 9 lessons for innovation in marketing from the world’s leading advertising agency. McGraw-Hill, New York (2011)

    Google Scholar 

  3. Sashi, C.M.: Customer engagement, buyer-seller relationships, and social media. Manage. Decis. 50(2), 253–272 (2012)

    Article  Google Scholar 

  4. Viswanath, B., et al.: On the evolution of user interaction in Facebook. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 37–42. ACM, Barcelona (2009)

    Google Scholar 

  5. Chu, S.-C.: Viral advertising in social media. J. Interact. Advertising 12(1), 30–43 (2011)

    Article  Google Scholar 

  6. Kim, D.H., et al.: Are you on timeline or news feed? the roles of facebook pages and construal level in increasing ad effectiveness. Comput. Hum. Behav. 57, 312–320 (2016)

    Article  Google Scholar 

  7. Mangold, W.G., Faulds, D.J.: Social media: the new hybrid element of the promotion mix. Bus. Horiz. 52(4), 357–365 (2009)

    Article  Google Scholar 

  8. Wang, R., Jin, Y.: An empirical study on the relationship between the followers’ number and influence of microblogging. In: 2010 International Conference on E-Business and E-Government (ICEE) (2010)

    Google Scholar 

  9. Wei, P.-S., Lu, H.-P.: An examination of the celebrity endorsements and online customer reviews influence female consumers’ shopping behavior. Comput. Hum. Behav. 29(1), 193–201 (2013)

    Article  Google Scholar 

  10. Tang, R., et al.: A study of short-term effect measurement for information publication in government microblog. Int. J. Hybrid Inf. Technol. 7(1), 57–66 (2014)

    Google Scholar 

  11. Zhiqin, D.: Research into factors affecting the attitudes of university students towards WeChat marketing based on AISAS mode. In: 2015 IEEE International Conference on Electro/Information Technology (EIT). IEEE (2015)

    Google Scholar 

  12. Hendriyani, J.J., et al.: Online consumer behavior: confirming the AISAS model on twitter users

    Google Scholar 

  13. Wang, Y., Xu, P.: Micro-blogging marketing based on the characteristics of network consumer. In: 2012 International Conference on Engineering and Business Management (2012)

    Google Scholar 

  14. Press, S.J.: Bayesian Statistics: Principles, Models, and Applications. Wiley, Hoboken (1989)

    MATH  Google Scholar 

  15. Jensen, F.V.: An introduction to Bayesian Networks, vol. 210. UCL Press, London (1996)

    Google Scholar 

  16. Stuart, A., Ord, K.: Kendall’s Advanced Theory of Statistics: Distribution Theory, vol. 1. Wiley, Hoboken (2009)

    Google Scholar 

  17. Murty, M.N., Devi, V.S.: Pattern Recognition: An Algorithmic Approach. Springer, Heidelberg (2011)

    Book  MATH  Google Scholar 

  18. Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, pp. 519–528. ACM, Budapest (2003)

    Google Scholar 

  19. Chamlertwat, W., et al.: Discovering consumer insight from twitter via sentiment analysis. J. UCS 18(8), 973–992 (2012)

    Google Scholar 

  20. Brier, G.W.: Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78(1), 1–3 (1950)

    Article  Google Scholar 

  21. Peffers, K., et al.: The design science research process: a model for producing and presenting information systems research. In: Proceedings of the First International Conference on Design Science Research in Information Systems and Technology (DESRIST 2006) (2006)

    Google Scholar 

  22. Triepels, R., Daniels, H.: Detecting shipping fraud in global supply chains using probabilistic trajectory classification (2015)

    Google Scholar 

  23. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Technopreneurship and Innovation Management Program, Chulalongkorn University, Bangkok, Thailand

    Pornpimon Kachamas

Authors
  1. Pornpimon Kachamas
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Correspondence to Pornpimon Kachamas .

Editor information

Editors and Affiliations

  1. University of Tasmania, Hobart, Australia

    Byeong Ho Kang

  2. Auckland University of Technology, Auckland, New Zealand

    Quan Bai

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Kachamas, P. (2016). Shaping Interactive Marketing Communication (IMC) Through Social Media Analytics and Modelling. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_59

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  • DOI: https://doi.org/10.1007/978-3-319-50127-7_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50126-0

  • Online ISBN: 978-3-319-50127-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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