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Social Media: The Dark Horse of Market in Consumer Decision Journey

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Big Data Analytics in Cognitive Social Media and Literary Texts

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Abstract

With businesses continually striving to be insightful enough to precisely read the consumers’ desires and subjectively measure their satisfaction, the need for and impact of Big Data Analytics is growing without bounds. The chapter elucidates the proficiency of behavioral predictions enabled through the big data further made available by people on various social media platforms, which guide the marketers throughout the Consumer Decision Journey. These behavioral predictions help the businesses to keep tapping the consumer decision journey through several customer touch-points. Consequently, Big Data Analytics being a massive source of data collection drives marketers toward value creation by comprehending the needs of customers effectively and thus promoting value delivery to customers. With the help of various social media models, the chapter also unravels the benefits of behavioral predictions to not only marketers but also other researchers in the field of health, politics, academia, and so on focusing on personality traits, lifestyle and health of their subjects and not just their buying behavior. The chapter explores the various dimensions of social media platforms and models to demonstrate effective use of technically enhanced opportunities to drive customer satisfaction.

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References

  • Abbas, J., Aman, J., Nurunnabi, M., & Bano, S. (2019). The impact of social media on learning behavior for sustainable education: Evidence of students from selected universities in Pakistan. Sustainability, 11(6), 1683.

    Article  Google Scholar 

  • Abramyk, H. (2020). Top 10 review websites to get more customer reviews on 2020. Retrieved June 24, 2020 from https://www.vendasta.com/blog/top-10-customer-review-websites.

  • Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34–49.

    Article  Google Scholar 

  • Baars, H., & Kemper, H. G. (2008). Management support with structured and unstructured data—an integrated business intelligence framework. Information Systems Management, 25(2), 132–148.

    Article  Google Scholar 

  • Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45–59.

    Article  Google Scholar 

  • Bi, G., Zheng, B., & Liu, H. (2014) Secondary crisis communication on social media: The role of corporate response and social influence in product-harm crisis. In PACIS (p. 93).

    Google Scholar 

  • Bleier, A., & Eisenbeiss, M. (2015). The importance of trust for personalized online advertising. Journal of Retailing, 91(3), 390–409.

    Article  Google Scholar 

  • Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017). Online behavioral advertising: A literature review and research agenda. Journal of Advertising, 46(3), 363–376.

    Article  Google Scholar 

  • Broussard, G. (2000). How advertising frequency can work to build online advertising effectiveness. International Journal of Market Research, 42(4), 1–13.

    Google Scholar 

  • Chan, N. L., & Guillet, B. D. (2011). Investigation of social media marketing: How does the hotel industry in Hong Kong perform in marketing on social media websites? Journal of Travel & Tourism Marketing, 28(4), 345–368.

    Article  Google Scholar 

  • Coles, L. (2014). Marketing with social media: 10 easy steps to success for business. Wiley.

    Google Scholar 

  • Court, D., Elzinga, D., Mulder, S., & Vetvik, O. J. (2009). The consumer decision journey. Retrieved June 20, 2020 from http://www.mckinseyquarterly.com/The_consumer_decision_journey_2373.

  • Cvijikj, I. P., & Michahelles, F. (2013). Online engagement factors on Facebook brand pages. Social Network Analysis and Mining, 3(4), 843–861.

    Article  Google Scholar 

  • Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.

    Article  Google Scholar 

  • Edelman, D. C. (2010). Branding in the digital age. Harvard Business Review, 88(12), 62–69.

    Google Scholar 

  • Fernández, A., del Río, S., López, V., Bawakid, A., del Jesus, M. J., Benítez, J. M., & Herrera, F. (2014). Big Data with Cloud Computing: An insight on the computing environment, MapReduce, and programming frameworks, Wiley Interdisciplinary reviews. Data Mining and Knowledge Discovery, 4(5), 380–409.

    Article  Google Scholar 

  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.

    Article  Google Scholar 

  • Gârdan, I. P., & Gârdan, D. A. (2014). The G.A.P. model applied to dental healthcare services. In The Proceedings of the International Conference “Marketing-from Information to Decision” (p. 107). Babes Bolyai University.

    Google Scholar 

  • Ghani, N. A., Hamid, S., Hashem, I. A. T., & Ahmed, E. (2019). Social media big data analytics: A survey. Computers in Human Behavior, 101, 417–428.

    Article  Google Scholar 

  • Goel, S., & Goldstein, D. G. (2014). Predicting individual behavior with social networks. Marketing Science, 33(1), 82–93.

    Article  Google Scholar 

  • Griffiths, M., & McLean, R. (2015). Unleashing corporate communications via social media: A UK study of brand management and conversations with customers. Journal of Customer Behaviour, 14(2), 147–162.

    Article  Google Scholar 

  • Gundecha, P., & Liu, H. (2012). Mining social media: A brief introduction. In New directions in informatics, optimization, logistics, and production (pp. 1–17). Informs.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hennig-Thurau, T., Malthouse, E. C., Friege, C., Gensler, S., Lobschat, L., Rangaswamy, A., & Skiera, B. (2010). The impact of new media on customer relationships. Journal of Service Research, 13(3), 311–330.

    Article  Google Scholar 

  • Hudson, S., & Thal, K. (2013). The impact of social media on the consumer decision process: Implications for tourism marketing. Journal of Travel & Tourism Marketing, 30(1–2), 156–160.

    Article  Google Scholar 

  • Jamie. (2019). 65+ social networking sites you need to know about. Retrieved June 23, 2020 from https://makeawebsitehub.com/social-media-sites/.

  • Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013). January big data: Issues and challenges moving forward. In 2013 46th Hawaii international conference on system sciences (pp. 995–1004). IEEE.

    Google Scholar 

  • Khan, Z., & Vorley, T. (2017) Big data text analytics: An enabler of knowledge management. Journal of Knowledge Management.

    Google Scholar 

  • Krishnan, M. (2018). 3 Models (and Tools) to understand, predict, and react to your social media. Retrieved June 20, 2020 from https://contentmarketinginstitute.com/2018/05/models-tools-social-media/.

  • Kumar, V., & Gupta, S. (2016). Conceptualizing the evolution and future of advertising. Journal of Advertising, 45(3), 302–317.

    Article  Google Scholar 

  • Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press.

    Google Scholar 

  • Manca, S., Caviglione, L., & Raffaghelli, J. (2016). Big data for social media learning analytics: Potentials and challenges. Journal of e-Learning and Knowledge Society, 12(2).

    Google Scholar 

  • McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2014). Big data and social media analytics. Psychological Methods, 9(4), 403–425.

    Article  Google Scholar 

  • Moniruzzaman, A. B. M., & Hossain, S. A. (2013). Nosql database: New era of databases for big data analytics-classification, characteristics and comparison. arXiv:1307.0191.

  • Neslin, S. A., Grewal, D., Leghorn, R., Shankar, V., Teerling, M. L., Thomas, J. S., & Verhoef, P. C. (2006). Challenges and opportunities in multichannel customer management. Journal of Service Research, 9(2), 95–112.

    Article  Google Scholar 

  • OECD, DDIBD. (2015). For growth and well-being: Big data for growth and well-being.

    Google Scholar 

  • Pan, B., MacLaurin, T., & Crotts, J. C. (2007) Travel blogs and the implications for destination marketing. Journal of Travel Research, 46(1), 35–45.

    Google Scholar 

  • Parashara, A., Parasharb, A., & Goyalc, S. (2018). Big data analysis using machine learning approach to compute data. Data Intensive Computing Applications for Big Data, 29, 133.

    Google Scholar 

  • Phillips, L., Dowling, C., Shaffer, K., Hodas, N., & Volkova, S. (2017). Using social media to predict the future: A systematic literature review. arXiv:1706.06134.

  • Preoţiuc-Pietro, D., Liu, Y., Hopkins, D., & Ungar, L. (July 2017) Beyond binary labels: Political ideology prediction of twitter users. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers) (pp. 729–740).

    Google Scholar 

  • Russell, J. (2013). The 15 best blogging and publishing platforms on the Internet today. Which blog is for you? Retrieved June 23, 1010 from https://thenextweb.com/businessapps/2013/08/16/best-blogging-services/.

  • Schindler, R. M., & Bickart, B. (2005). Published word of mouth: Referable, consumer-generated information on the internet. Online Consumer Psychology: Understanding and Influencing Consumer Behavior in the Virtual World, 32, 35–61.

    Google Scholar 

  • Schultz, D. (2016). The future of advertising or whatever we’re going to call it. Journal of Advertising, 45(3), 276–285.

    Article  Google Scholar 

  • Shostack, L. (1984). Designing services that deliver. Harvard Business Review, 62(1), 133–139.

    Google Scholar 

  • Simpao, A. F., Ahumada, L. M., Gálvez, J. A., & Rehman, M. A. (2014). A review of analytics and clinical informatics in health care. Journal of Medical Systems, 38(4), 45.

    Article  Google Scholar 

  • Skoric, M. M., Liu, J., & Jaidka, K. (2020). Electoral and public opinion forecasts with social media data: A meta-analysis. Information, 11(4), 187.

    Google Scholar 

  • Smith, T., Coyle, J. R., Lightfoot, E., & Scott, A. (2007). Reconsidering models of influence: The relationship between consumer social networks and word-of-mouth effectiveness. Journal of advertising research, 47(4), 387–397.

    Google Scholar 

  • Song, S. K., Kim, D. J., Hwang, M., Kim, J., Jeong, D. H., Lee, S., Jung, H., & Sung, W. (December 2013). Prescriptive analytics system for improving research power. In 2013 IEEE 16th international conference on computational science and engineering (pp. 1144–1145).

    Google Scholar 

  • Tsou, M. H. (2015). Research challenges and opportunities in mapping social media and Big Data. Cartography and Geographic Information Science, 42(sup1), 70–74.

    Article  Google Scholar 

  • Verhoef, P. C., Stephen, A. T., Kannan, P. K., Luo, X., Abhishek, V., Andrews, M., et al. (2017). Consumer connectivity in a complex, technology-enabled, and mobile-oriented world with smart products. Journal of Interactive Marketing, 40, 1–8.

    Google Scholar 

  • Vieira, C., Parsons, P., & Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers & Education, 122, 119–135.

    Article  Google Scholar 

  • Wen, M., Xia, Z., & Vasthimal, D. K. (October 2019). Practical lessons from predicting new user demographics for ad targeting. In 2nd workshop on online recommender systems and user modeling (pp. 59–67).

    Google Scholar 

  • Young, S. D. (2014). Behavioral insights on big data: Using social media for predicting biomedical outcomes. Trends in Microbiology, 22(11), 601–602.

    Article  Google Scholar 

  • Zachos, G., Paraskevopoulou-Kollia, E. A., & Anagnostopoulos, I. (2018). Social media use in higher education: A review. Education Sciences, 8(4), 194.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Academia World News/Institute of Strategy, Entrepreneurship and Education for Growth (iSEEG), Paphos, Cyprus

    Nipun Dhaulta

  2. Institute of Strategy, Entrepreneurship and Education for Growth (iSEEG), Paphos, Cyprus

    Sakshi Aggarwal

Authors
  1. Nipun Dhaulta
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  2. Sakshi Aggarwal
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Editor information

Editors and Affiliations

  1. CSE&IT, Madhav Institute of Technology & Science, Gwalior, India

    Sanjiv Sharma

  2. Madhav Institute of Technology & Science, Gwalior, India

    Valiur Rahaman

  3. Electronics & Communications Engineering, Myanmar Institute of Information Technology, Mandalay, Myanmar

    G. R. Sinha

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Dhaulta, N., Aggarwal, S. (2021). Social Media: The Dark Horse of Market in Consumer Decision Journey. In: Sharma, S., Rahaman, V., Sinha, G.R. (eds) Big Data Analytics in Cognitive Social Media and Literary Texts. Springer, Singapore. https://doi.org/10.1007/978-981-16-4729-1_16

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