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Analysing the Role of User Generated Content on Consumer Purchase Intention in the New Era of Social Media and Big Data


  • School of Management Studies, Vels University, Pallavaram, Chennai - 600117, Tamil Nadu, India


Objective: Big Data refers to the overwhelming amount of data that is being captured today by society, computers, cell phones and the internet. These data sets are so large and are of varied in nature, type and format that it becomes difficult to actually capture, manage, analyze, transform, model and organize this unstructured data for realizing company’s goal of discovering information and gain insights into consumer purchasing behavior. The paper attempts to offer this understanding of insights into consumer’s requirements through studying this social media big data. Methods/Statistical Analysis: The paper proposes that Social Media and Big Data are related to development of consumer purchase behavior. The unstructured data that is generated also known as User Generated Data (UGC) plays a very important role in forming consumer purchase intention. Findings: Through this study it was found that the new paradigm shift in the consumer’s purchase intention is driven by Social Media and Big Data. The researcher has found a perfect model fit using Structural Equation Modeling and proven through hypothesis that Social Media and Big data combined together are responsible to generation of UGC’s which impact purchase intention of consumers. Application/Improvement: the paper proposes that social media and big data are intersecting each other in a novel way and new methods and techniques need to be developed in order to get better insights into the unstructured data so that consumer requirements are better understood by marketers.


Big Data, Consumer Behavior, Purchase Intension, Social Media.

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  • Dellarocas XM, Zhang NF, Awad A. Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing. 2007; 21(4):23–45.
  • Reiss PC, Wolak FA. Structural econometric modeling: rationales and examples from industrial organization. Chapter 64 Handbook of Econometrics; 2007. p. 6.
  • Predictive-analytics-for-the-small-business [Internet]. [cited 2016 Apr 12]. Available from:
  • Garg R, Smith MD, Telang R. Measuring information diffusion in an online community. Journal of Management Information Systems. 2011; 28(2):11–38.
  • Russom P. Big data analytics, Best Practices Report, Fourth Quarter, The Data Warehouse Institute, Renton, WA; 2011 Sep.
  • Bhattacharjee S, Gopal RD, Lertwachara K, Marsden JR, Telang R. The effect of digital sharing technologies on music markets: A survival analysis of albums on ranking charts. Management Science. 2007; 53(9):1359–74.
  • Kauffman RJ, Wood CA. Revolutionary research strategies for e-business: A philosophy of science view in the age of the Internet, economics. Chapter 2. In: Kauffman RJ, Tallon PP, editors. Economics, Information Systems, and Electronic Commerce: Empirical Research, M. E. Sharpe: Armonk, NY; 2009. p. 31–62.
  • Aral S, Walker D. Identifying social influence in networks using randomized experiments. IEEE Intelligent Systems. 2011; 26(5):91–6.
  • Davenport TH, Harris JG. Competing on analytics: The new science of winning, Harvard Business Press: Boston, MA; 2007.
  • Boyer KK, Swink M L. Empirical elephants—why multiple methods are essential to quality research in operations and supply chain management. Journal of Operations Management. 2008; 26(3):338–44.
  • Tangpong C. Content analytic approach to measuring constructs in operations and supply chain management. Journal of Operations Management. 2011; 29(6):627–38.
  • Jick T D. Mixing qualitative and quantitative methods: Triangulation in action. Administrative Science Quarterly. 1979; 24(4):602–11.
  • Tuten TL. Advertising 2.0: Social media marketing in a web 2.0 world. Westport, CT: Greenwood Publishing Group; 2008.
  • Mittal P, Garg S, Yadav S. Social network analysis using interest mining: A critical review. Indian Journal of Science and Technology. 2016; 9(16):1–8.
  • Accenture. Retail technology vision [Internet]. 2013. [cited 2015 May 27]. Available from:
  • Anderson C. The end of the theory: the data deluge makes the scientific method obsolete [Internet]. 2008 [cited 2008 Jun 23]. Available from: html.
  • Kaisler S, Armour F, Espinosa JA, Money W. Big data: Issues and challenges moving forward, 46th Hawaii International Conference System Sciences (HICSS); 2013. p. 995–1004.
  • Eynon R. The rise of Big Data: What does it mean for education, technology, and media research? Learn. Media Technology. 2013; 38(3):237–40.
  • Liu X, Wang Y, Zhao D, Zhang W, Shi L. Patching by automatically tending to hub nodes based on social trust. Computer Standards & Interfaces. 2015; 44:94–101.
  • Cuzzocrea A, Song IY, Davis KC. Analytics over large-scale multidimensional data: The big data revolution! Proceedings of the ACM 14th International Workshop on Data Warehousing and OLAP; 2011. p. 101–4.
  • Milne D, Witten IH. An open-source toolkit for mining Wikipedia. Artificial Intelligence. 2013; 194:222–39.
  • Reips UD, Garaizar P. Mining twitter: A source for psychological wisdom of the crowds. Behavior Research Methods. 2011; 43(3):635–42.
  • Daugherty T, Eastin MS, Bright L. Exploring consumer motivations for creating user-generated content. Journal of Interactive Advertising. 2008; 8(2):16–25.
  • Smith AN, Fischer E, Yongjian C. How does brand-related user generated content differ across YouTube, Facebook, and Twitter? Journal of Interactive Marketing. 2015; 26(2):102–13.
  • Riegner C. Word of mouth on the web: The impact of web 2.0 on consumer purchase decisions. Journal of Advertising. 2007; 47(4):436–47.
  • Wang YJ, Minor MS, Wei J. Aesthetics and the online shopping environment: Understanding consumer responses. Journal of Retailing. 2011; 87(1):46–58.
  • Yang Z, Cai S, Zhou Z, Zhou N. Development and validation of an instrument to measure user perceived service quality of information presenting web portals. Information and Management. 2005; 42(4):575–89.
  • Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall; 1998.
  • Bentler PM, Chou CP. Practical issues in structural modeling. Sociological Methods and Research. 1987; 16(1):78–117.
  • Fornell C, Larcker DF. Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research. 1981; 18(3):382–8.
  • Brown JJ, Reingen PH. Social ties and word-of-mouth referral behavior. Journal Consumer Research. 1987; 14(3):350–62.


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